{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas.tseries.offsets import DateOffset\n",
    "\n",
    "import alphalens \n",
    "from alphalens.utils import print_table\n",
    "\n",
    "import json\n",
    "from tqdm import *\n",
    "from jqdata import *\n",
    "import tushare as ts\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn')\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据获取\n",
    "\n",
    "## 构造数据获取函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 获取基金基础信息\n",
    "def GetFundInfo(start_date:str,end_date:str)->pd.DataFrame:\n",
    "    \n",
    "    # 按年度获取\n",
    "    start_period = pd.date_range(start_date,end_date,freq='YS')\n",
    "    end_period = pd.date_range(start_date,end_date,freq='Y')\n",
    "\n",
    "    df_list = []\n",
    "    \n",
    "    for start,end in zip(start_period,end_period):\n",
    "        \n",
    "        df_list.append(Query_Fund_Info(start,end))\n",
    "    \n",
    "    return pd.concat(df_list)\n",
    "\n",
    "# jq数据基金分类之区分股票和混合 没有更细的分类\n",
    "def Query_Fund_Info(start_date: str, end_date: str) -> pd.DataFrame:\n",
    "    '''获取某时间段中开放式基金中的股票型、混合型基金数据'''\n",
    "\n",
    "    q = query(finance.FUND_MAIN_INFO).filter(finance.FUND_MAIN_INFO.operate_mode == '开放式基金',\n",
    "                                             finance.FUND_MAIN_INFO.underlying_asset_type.in_(\n",
    "                                                 ['股票型', '混合型']),\n",
    "                                             finance.FUND_MAIN_INFO.start_date >= start_date,\n",
    "                                             finance.FUND_MAIN_INFO.start_date <= end_date)\n",
    "\n",
    "    return finance.run_query(q)\n",
    "\n",
    "# 获取基金规模数据\n",
    "def GetFundTotalAsset(ts_code:list,start_date:str,end_date:str)->pd.DataFrame:\n",
    "    \n",
    "    limit_codes = 3000  # 不能大于3000行\n",
    "    \n",
    "    if len(ts_code) <= limit_codes:\n",
    "    \n",
    "        df = Query_fund_total_asset(ts_code, start_date,end_date)\n",
    "        \n",
    "    else:\n",
    "\n",
    "        code_list = [ts_code[i:i + limit_codes]\n",
    "                     for i in range(0, len(ts_code), limit_codes)]\n",
    "\n",
    "        df_temp = []\n",
    "\n",
    "        for code in tqdm(code_list,desc='查询基金规模'):\n",
    "\n",
    "            df_temp.append(Query_fund_total_asset(code, start_date,end_date))\n",
    "\n",
    "        df = pd.concat(df_temp)\n",
    "      \n",
    "    return df\n",
    "\n",
    "\n",
    "def Query_fund_total_asset(codes: list, start_date: str,end_date:str) -> pd.DataFrame:\n",
    "    \n",
    "    # 按季度获取\n",
    "    periods = pd.date_range(start_date,end_date,freq='Q')\n",
    "\n",
    "    df_list = []\n",
    "    \n",
    "    for rptdate in periods:\n",
    "        \n",
    "        q = query(finance.FUND_PORTFOLIO.code,\n",
    "              finance.FUND_PORTFOLIO.period_end,\n",
    "              finance.FUND_PORTFOLIO.pub_date,\n",
    "              finance.FUND_PORTFOLIO.report_type,\n",
    "              finance.FUND_PORTFOLIO.total_asset).filter(\n",
    "                  finance.FUND_PORTFOLIO.code.in_(codes),\n",
    "                  finance.FUND_PORTFOLIO.period_end == rptdate)\n",
    "        \n",
    "        df_list.append(finance.run_query(q))\n",
    "    \n",
    "    df = pd.concat(df_list).reset_index(drop=True)\n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "# 批量查询\n",
    "def GetFundsNav(main_code_list:str,start_date:str,end_date:str):\n",
    "    \n",
    "    # 按季度将所有数据拼接在一起\n",
    "    # 每季度的交易日按60日算 限制为3000行 那么main_code需要分为每组50个\n",
    "    # 才能保证最获取到所有数据\n",
    "    \n",
    "    temp_list = []\n",
    "    \n",
    "    N = 50\n",
    "    \n",
    "    codes = [main_code[i:i+N] for i in range(0,len(main_code),N)]\n",
    "    \n",
    "    for code in tqdm(codes,desc='查询基金净值'):\n",
    "        \n",
    "        temp_list.append(Query_Fund_Nav(code,start_date,end_date))\n",
    "        \n",
    "    fund_df = pd.concat(temp_list,ignore_index=True)\n",
    "    \n",
    "    return fund_df\n",
    "        \n",
    "\n",
    "# 查询基金累计复权净值\n",
    "def Query_Fund_Nav(main_code:list,start_date:str,end_date:str)->pd.DataFrame:\n",
    "    \n",
    "    \n",
    "    start_period = pd.date_range(start_date,end_date,freq='QS')\n",
    "    end_period = pd.date_range(start_date,end_date,freq='Q')\n",
    "    \n",
    "    df_list = []\n",
    "    for start,end in zip(start_period,end_period):\n",
    "        q = query(finance.FUND_NET_VALUE.code,finance.FUND_NET_VALUE.day,\n",
    "                  finance.FUND_NET_VALUE.refactor_net_value).filter(\n",
    "            finance.FUND_NET_VALUE.code.in_(main_code),finance.FUND_NET_VALUE.day>=start,\n",
    "            finance.FUND_NET_VALUE.day<=end)\n",
    "        \n",
    "        df_list.append(finance.run_query(q))\n",
    "        \n",
    "    return pd.concat(df_list,ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据获取\n",
    "\n",
    "由于JQData公募基金分类仅区分大类品种,按照如下进行筛选可能更好:\n",
    "\n",
    ">Wind基金类型分类选择普通股票型基金以及偏股混合型基金\n",
    "\n",
    "所以在这里我们仅筛选出*股票型*和*混合型*基金进行分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置时间范围\n",
    "start_date,end_date = '2007-01-01','2020-03-31'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "共计4248支基金\n"
     ]
    }
   ],
   "source": [
    "# 获取基金基础数据\n",
    "fund_info = GetFundInfo(start_date,end_date)\n",
    "\n",
    "main_code = fund_info['main_code'].unique().tolist()\n",
    "\n",
    "print('共计%s支基金'%len(main_code))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "查询基金规模: 100%|██████████| 2/2 [00:17<00:00, 10.14s/it]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>period_end</th>\n",
       "      <th>pub_date</th>\n",
       "      <th>report_type</th>\n",
       "      <th>total_asset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>020018</td>\n",
       "      <td>2007-03-31</td>\n",
       "      <td>2007-04-18</td>\n",
       "      <td>第一季度</td>\n",
       "      <td>1.644130e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>213003</td>\n",
       "      <td>2007-03-31</td>\n",
       "      <td>2007-04-21</td>\n",
       "      <td>第一季度</td>\n",
       "      <td>1.047415e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>257030</td>\n",
       "      <td>2007-03-31</td>\n",
       "      <td>2007-04-20</td>\n",
       "      <td>第一季度</td>\n",
       "      <td>4.622084e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>519017</td>\n",
       "      <td>2007-03-31</td>\n",
       "      <td>2007-04-20</td>\n",
       "      <td>第一季度</td>\n",
       "      <td>1.267888e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>020018</td>\n",
       "      <td>2007-06-30</td>\n",
       "      <td>2007-07-19</td>\n",
       "      <td>第二季度</td>\n",
       "      <td>1.935767e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     code  period_end    pub_date report_type   total_asset\n",
       "0  020018  2007-03-31  2007-04-18        第一季度  1.644130e+09\n",
       "1  213003  2007-03-31  2007-04-21        第一季度  1.047415e+10\n",
       "2  257030  2007-03-31  2007-04-20        第一季度  4.622084e+09\n",
       "3  519017  2007-03-31  2007-04-20        第一季度  1.267888e+10\n",
       "4  020018  2007-06-30  2007-07-19        第二季度  1.935767e+09"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取基金规模数据(季度)\n",
    "fund_share = GetFundTotalAsset(main_code,start_date,end_date)\n",
    "\n",
    "fund_share.to_csv('../Data/fund_share.csv')\n",
    "\n",
    "fund_share.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "查询基金净值: 100%|██████████| 85/85 [03:19<00:00,  1.54s/it]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>day</th>\n",
       "      <th>refactor_net_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>213003</td>\n",
       "      <td>2007-01-19</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>213003</td>\n",
       "      <td>2007-01-26</td>\n",
       "      <td>1.0015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>213003</td>\n",
       "      <td>2007-01-29</td>\n",
       "      <td>1.0061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>213003</td>\n",
       "      <td>2007-01-30</td>\n",
       "      <td>1.0044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>213003</td>\n",
       "      <td>2007-01-31</td>\n",
       "      <td>0.9937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     code         day  refactor_net_value\n",
       "0  213003  2007-01-19              1.0000\n",
       "1  213003  2007-01-26              1.0015\n",
       "2  213003  2007-01-29              1.0061\n",
       "3  213003  2007-01-30              1.0044\n",
       "4  213003  2007-01-31              0.9937"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取基金\n",
    "found_nav = GetFundsNav(main_code,start_date,end_date)\n",
    "\n",
    "found_nav.to_csv('../Data/fund_nav.csv')\n",
    "\n",
    "found_nav.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 优选基金\n",
    "\n",
    "## 基金规模初选\n",
    "\n",
    "**基金规模是评价基金产品的重要维度之一。我们基金选择的第一步，就是根据基金规模进行初选**。我们按照基金当期规模排序分十组，考察各组在下季度的收益情况（使用最新一期规模）。分组超额（以等权持有当季的所以基金的收益为基准）收益结果显示，**基金收益表现会受到基金规模的影响， 但二者并非线性关系，而是呈现右偏的倒U形。规模最大的基金明显跑输其他基金，平均一个季度贡献-1.25%的负向超额收益。中等规模基金表现最好。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/numpy/lib/arraysetops.py:472: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    }
   ],
   "source": [
    "# 数据读取\n",
    "fund_share = pd.read_csv('../Data/fund_share.csv', index_col=0,\n",
    "                         dtype={'code': str}, parse_dates=['period_end'])\n",
    "                         \n",
    "fund_nav = pd.read_csv('../Data/fund_nav.csv', index_col=0,\n",
    "                       dtype={'code': str}, parse_dates=['day'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# step 0:\n",
    "\n",
    "def quick_prepossessing(fund_nav: pd.DataFrame, fund_share: pd.DataFrame, start_date: str, end_date: str) -> pd.DataFrame:\n",
    "    \n",
    "    '''数据对齐'''\n",
    "    \n",
    "    fund_nav = fund_nav.copy()\n",
    "\n",
    "    # 计算季度收益 Ser level0-day level1-code\n",
    "    q_ret = fund_nav.groupby([pd.Grouper(key='day', freq='Q'), 'code'])[\n",
    "        'refactor_net_value'].apply(Cala_Ret)\n",
    "    q_next_ret = q_ret.groupby(level='code').transform(lambda x: x.shift(-1))\n",
    "    q_next_ret.name = 'NEXT_RET'\n",
    "\n",
    "    # 计算近一年夏普\n",
    "    q_sharpe = GetSharpe(fund_nav, start_date, end_date)\n",
    "\n",
    "    # 规模数据\n",
    "    q_share = fund_share.groupby([pd.Grouper(key='period_end', freq='Q'), 'code'])[\n",
    "        'total_asset'].last()\n",
    "\n",
    "    df = pd.concat([q_next_ret, q_sharpe, q_share], axis=1, join='inner')  # 合并\n",
    "    df.dropna(inplace=True)\n",
    "    df.index.names = ['date', 'code']\n",
    "    df.rename(columns={\"total_asset\": 'total_asset'.upper()}, inplace=True)\n",
    "\n",
    "    return df.reset_index()\n",
    "\n",
    "# step 1:\n",
    "def GetSharpe(fund_nav: pd.DataFrame, start_date: str, end_date: str) -> pd.Series:\n",
    "    \n",
    "    '''计算每季度末近一年的夏普'''\n",
    "    \n",
    "    periods = pd.date_range(start_date, end_date, freq='Q')\n",
    "\n",
    "    pivot_nav = pd.pivot_table(\n",
    "        fund_nav, index='day', columns='code', values='refactor_net_value')\n",
    "    idx = pivot_nav.index\n",
    "\n",
    "    rpt_trade_dic = idx.to_frame().resample('Q').last()\n",
    "    rpt_trade_dic = rpt_trade_dic['day'].to_dict()\n",
    "\n",
    "    df = pd.DataFrame(index=periods, columns=pivot_nav.columns)\n",
    "\n",
    "    for i in periods:\n",
    "\n",
    "        end = idx.get_loc(rpt_trade_dic[i])\n",
    "        start = end - 244\n",
    "\n",
    "        if start < 0:\n",
    "\n",
    "            df.loc[i, :] = np.nan\n",
    "\n",
    "        else:\n",
    "\n",
    "            df.loc[i, :] = Cala_Sharpe(pivot_nav.iloc[start:end, :])\n",
    "\n",
    "    ser = df.stack()\n",
    "    ser = ser.astype(float)\n",
    "    ser.name = 'sharpe'.upper()\n",
    "\n",
    "    return ser\n",
    "\n",
    "# step 2:\n",
    "def Cala_Sharpe(nav: pd.DataFrame) -> pd.Series:\n",
    "    \n",
    "    '''计算夏普'''\n",
    "    \n",
    "    per_nav = nav.shift(1)\n",
    "    ret = np.log1p(nav.pct_change())\n",
    "\n",
    "    cum = np.exp(ret.cumsum()) - 1\n",
    "    annual_ret = (1 + cum) ** (252 / len(cum)) - 1\n",
    "    annual_ret = annual_ret.iloc[-1]  # 年化收益率\n",
    "\n",
    "    volatility = ret.std() * np.sqrt(252) # 年化波动率\n",
    "\n",
    "    return ((annual_ret - 0.04) / volatility).replace([- np.inf, np.inf], np.nan)\n",
    "\n",
    "# step 3:\n",
    "def Cala_Ret(nav: pd.Series) -> float:\n",
    "    \n",
    "    '''计算累计收益'''\n",
    "    \n",
    "    return np.sum(np.log(nav / nav.shift(1))) # 累计收益率\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:85: RuntimeWarning: divide by zero encountered in log\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:70: RuntimeWarning: divide by zero encountered in log1p\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>code</th>\n",
       "      <th>NEXT_RET</th>\n",
       "      <th>SHARPE</th>\n",
       "      <th>TOTAL_ASSET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>000031</td>\n",
       "      <td>-0.094815</td>\n",
       "      <td>-0.700139</td>\n",
       "      <td>4.311237e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>020010</td>\n",
       "      <td>-0.170527</td>\n",
       "      <td>0.110247</td>\n",
       "      <td>5.063917e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>020011</td>\n",
       "      <td>-0.221723</td>\n",
       "      <td>0.091869</td>\n",
       "      <td>4.825745e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>040007</td>\n",
       "      <td>-0.239293</td>\n",
       "      <td>2.313625</td>\n",
       "      <td>1.669839e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>040008</td>\n",
       "      <td>-0.175549</td>\n",
       "      <td>0.999229</td>\n",
       "      <td>1.714130e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date    code  NEXT_RET    SHARPE   TOTAL_ASSET\n",
       "0 2008-03-31  000031 -0.094815 -0.700139  4.311237e+09\n",
       "1 2008-03-31  020010 -0.170527  0.110247  5.063917e+09\n",
       "2 2008-03-31  020011 -0.221723  0.091869  4.825745e+09\n",
       "3 2008-03-31  040007 -0.239293  2.313625  1.669839e+10\n",
       "4 2008-03-31  040008 -0.175549  0.999229  1.714130e+10"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 初始化数据\n",
    "df = quick_prepossessing(fund_nav,fund_share,start_date,end_date)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Get_Grop(df:pd.DataFrame,col:str):\n",
    "    \n",
    "    '''分组函数'''\n",
    "    \n",
    "    df_ = df.copy()\n",
    "    \n",
    "    label_name = [str(i) for i in range(1,11)]\n",
    "    df_['GROUP'] = pd.qcut(df_[col],10,labels=label_name)\n",
    "    \n",
    "    # 基准收益假设为等权持有当季度的所有基金的收益\n",
    "    #df.rename(columns={'refactor_net_value':'NEXT_RET'})\n",
    "    df_['EXCESS_RET'] = df_['NEXT_RET'] - df_['NEXT_RET'].mean()\n",
    "    \n",
    "    return df_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>code</th>\n",
       "      <th>NEXT_RET</th>\n",
       "      <th>SHARPE</th>\n",
       "      <th>TOTAL_ASSET</th>\n",
       "      <th>GROUP</th>\n",
       "      <th>EXCESS_RET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>000031</td>\n",
       "      <td>-0.094815</td>\n",
       "      <td>-0.700139</td>\n",
       "      <td>4.311237e+09</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.107831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>020010</td>\n",
       "      <td>-0.170527</td>\n",
       "      <td>0.110247</td>\n",
       "      <td>5.063917e+09</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.183544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>020011</td>\n",
       "      <td>-0.221723</td>\n",
       "      <td>0.091869</td>\n",
       "      <td>4.825745e+09</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.234740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>040007</td>\n",
       "      <td>-0.239293</td>\n",
       "      <td>2.313625</td>\n",
       "      <td>1.669839e+10</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.252310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>040008</td>\n",
       "      <td>-0.175549</td>\n",
       "      <td>0.999229</td>\n",
       "      <td>1.714130e+10</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.188566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date    code  NEXT_RET    ...       TOTAL_ASSET  GROUP EXCESS_RET\n",
       "0 2008-03-31  000031 -0.094815    ...      4.311237e+09     10  -0.107831\n",
       "1 2008-03-31  020010 -0.170527    ...      5.063917e+09     10  -0.183544\n",
       "2 2008-03-31  020011 -0.221723    ...      4.825745e+09     10  -0.234740\n",
       "3 2008-03-31  040007 -0.239293    ...      1.669839e+10     10  -0.252310\n",
       "4 2008-03-31  040008 -0.175549    ...      1.714130e+10     10  -0.188566\n",
       "\n",
       "[5 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按规模分组\n",
    "group_df = Get_Grop(df,'TOTAL_ASSET')\n",
    "\n",
    "group_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0e670c1ac8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.family']='serif'\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.title('分组超额收益（第一组为规模最小的组）')\n",
    "sns.barplot(x='GROUP',y='EXCESS_RET',data=group_df,ci=0,color='#DC143C')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一般来说，基金产品规模越大，意味投资者对基金经理能力的认可度越高，基金经理实力越强。 同时基金获取数据和研究服务的成本，以及管理成本和间接费用并不会随规模的增长同比例增加， 这些会给基金带来正的规模经济效应。然而实证结果显示，大规模基金未来业绩表现反而不佳。可能的原因有以下几点：**（1）流动性约束与价格冲击**：大规模交易使得基金调仓灵活性变差，可能 会错过买卖股票的最佳时机，而小基金“船小好调头”。此外大额交易带来的冲击成本也会提高。 **（2）基金经理管理能力限制**：基金投资受限于“双十规定”（一只基金持同一股票不得超过基金 资产的 10%，一个基金公司旗下所有基金持同一股票不得超过该股票市值的 10%。），被迫分散投资，很难集中仓位于少数几只精选股票，导致类指数化投资的尴尬。此外，规模做大后，维持业绩稳定是基金经理的首要目标，追求短期高收益的动力不足。**（3）打新收益稀释**：基金参与打新 可以增厚收益，而打到手的新股是有上限的，大规模基金的打新收益会被稀释。此外，**小规模基金 虽然也能带来一定超额收益，但流动性较差，资质良莠不齐，更易受到日常申赎的影响，业绩波动 可能较大。**\n",
    "\n",
    "综合考虑后，此处我们**选择规模大于 10 亿且小于 100 亿的中等规模基金作为研究对象**。每季度基金规模的剔除比例如下图所示。可以看到，2008 年之后，由于基金供给有限，供不应求使得基金规模迅速膨胀。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 总个数\n",
    "total_fund = df['date'].dt.strftime('%Y-%m').value_counts().sort_index()\n",
    "\n",
    "# 研究对象\n",
    "target_fund = df.query('TOTAL_ASSET > 10e8 and TOTAL_ASSET < 100e8')['date'].dt.strftime('%Y-%m').value_counts().sort_index()\n",
    "\n",
    "# 剔除比例\n",
    "percent = (1 - target_fund / total_fund)\n",
    "\n",
    "total_fund.plot.bar(figsize=(20,8),color='DimGray',title='基金规模剔除比例')\n",
    "plt.legend(['原样本数量'],loc='upper left')\n",
    "plt.ylabel('数量')\n",
    "plt.twinx()\n",
    "percent.plot(marker='o',color='r')\n",
    "plt.legend(['基金规模剔除比例'],loc='upper right')\n",
    "plt.ylabel('剔除比例')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选基指标\n",
    "\n",
    "我们使用**夏普比率作为选基指标**。夏普比率，是一个可以同时对收益与风险加以综合考虑的指标，广泛运用于基金市场，目前已成为国际上用以衡量基金绩效表现的最为常用的一个 标准化指标。夏普比率的核心思想就是，在固定的所能承受的风险时，追求最大报酬；在固定的预 期报酬下，追求最低的风险。下面我们在进行过规模筛选后的基金池中，**每季度末按照过去一年的夏普比率将基金分十组**，考察 top10%基金构成的等权基金组合相对于基金池的超额收益。可以看 到，季度调仓的高夏普比率基金组合自2008年以来，相对于等权基金池，平均可以取得 4%的年化超额收益，最近三年更是可以取得平均 6%的年化超额收益。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def View_SP(df:pd.DataFrame)->pd.DataFrame:\n",
    "    \n",
    "    '''选取夏普最高的TOP10 查看每年收益情况'''\n",
    "    \n",
    "    df_ = df.copy()\n",
    "    df_['date'] = pd.to_datetime(df_['date'])\n",
    "    \n",
    "    label_name = [str(i) for i in range(1,11)]\n",
    "    df_['GROUP'] = df_.groupby('date')['SHARPE'].transform(lambda x:pd.qcut(x,10,labels=label_name))\n",
    "    \n",
    "    df_['EXCESS_RET'] = df_.groupby('date')['NEXT_RET'].transform(lambda x:x - x.mean())\n",
    "    df_ = df_.query('GROUP == \"10\"')\n",
    "    excess_ret = df_.groupby(pd.Grouper(key='date',freq='Y'))['EXCESS_RET'].mean()\n",
    "    ret = df_.groupby(pd.Grouper(key='date',freq='Y'))['NEXT_RET'].mean()\n",
    "    \n",
    "    df = pd.concat([ret,excess_ret],axis=1)\n",
    "    df.columns = ['RET','EXCESS_RET']\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0e60f54390>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 筛选 基金规模\n",
    "target_fund = df.query('TOTAL_ASSET > 10e8 and TOTAL_ASSET < 100e8').copy()\n",
    "\n",
    "x_df = target_fund.groupby('date',group_keys=False).apply(Get_Grop,col='SHARPE')\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.title('基金当期夏普比率与下季度收益的分组回测结果')\n",
    "sns.barplot(x='GROUP',y='EXCESS_RET',data=x_df,ci=0,color='#DC143C')\n",
    "\n",
    "x_df = View_SP(target_fund)\n",
    "x_df.plot.bar(figsize=(18,8),\n",
    "              title='过去一年夏普比率 top10%基金等权组合的分年收益（季度调仓）'\n",
    "              ,color={'#DC143C','#696969'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 优选基金情况\n",
    "\n",
    "下图展示了我们每季度的优选基金的数量和平均规模。最近三年优选基金数量基本在 20 只30 只之间，平均规模基本在 20 亿-30 亿之间。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_name = [str(i) for i in range(1, 11)]\n",
    "\n",
    "target_fund['GROUP'] = target_fund.groupby('date')['SHARPE'].transform(\n",
    "    lambda x: pd.qcut(x, 10, labels=label_name))\n",
    "\n",
    "ys_fund = target_fund.query('GROUP == \"10\"')\n",
    "\n",
    "x_df = pd.concat([ys_fund.groupby('date')['SHARPE'].mean(),\n",
    "                  ys_fund['date'].value_counts()], axis=1)\n",
    "\n",
    "x_df.index = pd.Index(map(lambda x: x.strftime('%Y-%m'), x_df.index))\n",
    "\n",
    "x_df['SHARPE'].plot(figsize=(18, 8),\n",
    "                    title='优选基金的数量和平均规模',\n",
    "                    marker='o',\n",
    "                    color='#DC143C')\n",
    "                    \n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('规模')\n",
    "plt.legend(['每季度优秀基金平均规模'],loc='upper left')\n",
    "plt.twinx()\n",
    "x_df['date'].plot.bar(alpha=0.5, color='#696969')\n",
    "plt.ylabel('数量')\n",
    "plt.legend(['每季度优秀基金数'],loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 挑选优选基金的超配股\n",
    "\n",
    "\n",
    "## 优选基金的前十大重仓股\n",
    "\n",
    "所谓基金重仓股，是指该股票占基金净值较大比重，对基金净值具有较大影响。一般所指的基金重仓股，是单个基金在季报中披露的权重排名前十的股票。按照基金的报告披露规则，基金管理人应当在每个季度结束之日起十五个工作日内，编制完成基金季度报告，并公布其前十大重仓股， 这些信息具有相当的及时性。下面我们根据基金季报信息，统计优选基金的前十大重仓股。如果某 股票由多只基金持仓，则计算其平均权重。 \n",
    "\n",
    "**从优选基金组合的前十大重仓股的数量变化来看，最近三年重仓股数量明显降低，在200只左右，说明优选基金的抱团紧密度提高，心仪股票更加集中。从平均权重变化来看，重仓股平均持仓权重提高，当前在6%左右，说明单个基金的持仓分散程度降低。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GetHoldStocks(ys_fund:pd.DataFrame):\n",
    "    \n",
    "    df_list = []\n",
    "    \n",
    "    for rptdate,codes in tqdm(ys_fund.groupby('date')['code'],desc='查询持仓'):\n",
    "        \n",
    "        rptdate = rptdate.strftime('%Y-%m-%d')\n",
    "        \n",
    "        codes = codes.values.tolist()\n",
    "        \n",
    "        df_list.append(QueryHoldStocks(codes,rptdate))\n",
    "    \n",
    "    df = pd.concat(df_list,ignore_index=True)\n",
    "        \n",
    "    return df\n",
    "\n",
    "def QueryHoldStocks(codes: list, rptdate: str) -> pd.DataFrame:\n",
    "    \n",
    "    '''查询基金重仓股'''\n",
    "    \n",
    "    limit = 300\n",
    "    \n",
    "    if len(codes) <= limit:\n",
    "        \n",
    "        # 获取前十持仓\n",
    "        q = query(finance.FUND_PORTFOLIO_STOCK.code,\n",
    "                  finance.FUND_PORTFOLIO_STOCK.period_end,\n",
    "                  finance.FUND_PORTFOLIO_STOCK.pub_date,\n",
    "                  finance.FUND_PORTFOLIO_STOCK.symbol,\n",
    "                  finance.FUND_PORTFOLIO_STOCK.rank,\n",
    "                  finance.FUND_PORTFOLIO_STOCK.proportion,\n",
    "                  finance.FUND_PORTFOLIO_STOCK.report_type,\n",
    "                  finance.FUND_PORTFOLIO_STOCK.market_cap).filter(\n",
    "                      finance.FUND_PORTFOLIO_STOCK.code.in_(codes),\n",
    "                      finance.FUND_PORTFOLIO_STOCK.rank <= 10,\n",
    "                      finance.FUND_PORTFOLIO_STOCK.report_type.like('第%'),\n",
    "                      finance.FUND_PORTFOLIO_STOCK.period_end == rptdate)\n",
    "\n",
    "        df = finance.run_query(q)\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        codes = [codes[i:i+limit] for i in range(0,len(codes),limit)]\n",
    "        \n",
    "        df_list = []\n",
    "        \n",
    "        for code in codes:\n",
    "            \n",
    "            # 获取前十持仓\n",
    "            q = query(finance.FUND_PORTFOLIO_STOCK.code,\n",
    "                      finance.FUND_PORTFOLIO_STOCK.period_end,\n",
    "                      finance.FUND_PORTFOLIO_STOCK.pub_date,\n",
    "                      finance.FUND_PORTFOLIO_STOCK.symbol,\n",
    "                      finance.FUND_PORTFOLIO_STOCK.rank,\n",
    "                      finance.FUND_PORTFOLIO_STOCK.report_type,\n",
    "                      finance.FUND_PORTFOLIO_STOCK.proportion,\n",
    "                      finance.FUND_PORTFOLIO_STOCK.market_cap).filter(\n",
    "                          finance.FUND_PORTFOLIO_STOCK.code.in_(codes),\n",
    "                          finance.FUND_PORTFOLIO_STOCK.rank <= 10,\n",
    "                          finance.FUND_PORTFOLIO_STOCK.report_type.like('第%'),\n",
    "                          finance.FUND_PORTFOLIO_STOCK.period_end == rptdate)\n",
    "\n",
    "            df_list.append(finance.run_query(q))\n",
    "        \n",
    "        df = pd.concat(df_list,ignore_index=True)\n",
    "    \n",
    "    df['symbol'] = df['symbol'].str.replace(r'\\D','',regex=True) # JQdata symbol 有些地方没清洗干净 有后缀\n",
    "    \n",
    "    return df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "查询持仓: 100%|██████████| 48/48 [00:19<00:00,  3.48it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>period_end</th>\n",
       "      <th>pub_date</th>\n",
       "      <th>symbol</th>\n",
       "      <th>rank</th>\n",
       "      <th>proportion</th>\n",
       "      <th>report_type</th>\n",
       "      <th>market_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>002031</td>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>2009-01-23</td>\n",
       "      <td>000623</td>\n",
       "      <td>1</td>\n",
       "      <td>3.20</td>\n",
       "      <td>第四季度</td>\n",
       "      <td>52305830.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>002031</td>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>2009-01-23</td>\n",
       "      <td>600970</td>\n",
       "      <td>2</td>\n",
       "      <td>3.14</td>\n",
       "      <td>第四季度</td>\n",
       "      <td>51269952.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>002031</td>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>2009-01-23</td>\n",
       "      <td>600739</td>\n",
       "      <td>3</td>\n",
       "      <td>3.04</td>\n",
       "      <td>第四季度</td>\n",
       "      <td>49770174.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>002031</td>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>2009-01-23</td>\n",
       "      <td>000937</td>\n",
       "      <td>4</td>\n",
       "      <td>2.14</td>\n",
       "      <td>第四季度</td>\n",
       "      <td>35000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>002031</td>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>2009-01-23</td>\n",
       "      <td>600352</td>\n",
       "      <td>5</td>\n",
       "      <td>1.54</td>\n",
       "      <td>第四季度</td>\n",
       "      <td>25199622.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     code  period_end     ...      report_type   market_cap\n",
       "0  002031  2008-12-31     ...             第四季度  52305830.64\n",
       "1  002031  2008-12-31     ...             第四季度  51269952.00\n",
       "2  002031  2008-12-31     ...             第四季度  49770174.72\n",
       "3  002031  2008-12-31     ...             第四季度  35000000.00\n",
       "4  002031  2008-12-31     ...             第四季度  25199622.00\n",
       "\n",
       "[5 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据获取\n",
    "hold_df = GetHoldStocks(ys_fund)\n",
    "\n",
    "# 数据储存\n",
    "hold_df.to_csv('../Data/hold_df.csv')\n",
    "\n",
    "hold_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据读取\n",
    "hold_df = pd.read_csv('../Data/hold_df.csv',\n",
    "                      dtype={'code': str,'symbol':str},\n",
    "                      index_col=0,\n",
    "                      parse_dates=['period_end'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def View_Hold_Stock(hold_df:pd.DataFrame):\n",
    "    \n",
    "    hold_df = hold_df.copy()\n",
    "    \n",
    "    hold_df['period_end'] = pd.to_datetime(hold_df['period_end'])\n",
    "    hold_df['period_end'] = hold_df['period_end'].dt.strftime('%Y-%m')\n",
    "    \n",
    "    # 数量\n",
    "    stock_num = hold_df.groupby('period_end')['symbol'].apply(lambda x:len(set(x)))\n",
    "    proportion = hold_df.groupby('period_end')['proportion'].mean()\n",
    "    \n",
    "    \n",
    "    proportion.plot(figsize=(18, 8),\n",
    "                    title='优选基金组合的前十大重仓股的数量和平均权重',\n",
    "                    marker='o',\n",
    "                    color='#DC143C')\n",
    "                    \n",
    "    plt.xticks(rotation=90)\n",
    "    plt.ylabel('权重')\n",
    "    plt.legend(['优秀基金组合前十大重仓股平均权重'],loc='upper left')\n",
    "    plt.twinx()\n",
    "    stock_num.plot.bar(alpha=0.5, color='#696969')\n",
    "    plt.ylabel('数量')\n",
    "    plt.legend(['优秀基金组合前十大重仓股数量'],loc='upper right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "View_Hold_Stock(hold_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 优选基金的超配股\n",
    "\n",
    "**不同基金的持仓分散程度不同，持仓集中度高的股票，其重仓股更具代表性**。假设A，B基金均是优选基金，A基金的第一大权重股Sa占5%，B 基金的第一大权重股Sb占比达到 15%，则股票 Sb 更能代表基金B的业绩表现，更能反映基金经理的投资思路。因而，配置股票Sb 相比 配置股票 Sa 能够更好地抓住优秀基金经理带来的超额收益。因而，我们在优选基金组合的前十大重仓股中，做进一步的筛选，选出其中的超配股票。 \n",
    "\n",
    "考虑到不同基金的股票投资比例不尽相同，我们按照**持股市值占基金总股票投资市值的比例(这里JQdata中仅有股票占净值的占比,所以我们只能用此进行分析)**对股票进行排序。在优选基金的前十大重仓股票池中，再选权重排名前 10%的股票作为优选基金 组合的超配股。这些超配股票都是基金经理精选挑选的，具有强烈信心的战略性重仓配置品种，普遍具有明朗的成长前景和较高的投资价值，可以更好地反映基金经理的投资思路。在投资组合中合理配置这些超配股票，可以更好地捕捉到来自优秀基金经理的超额收益。 \n",
    "\n",
    "需要注意的是：如果某只股票被多只基金持有，且权重排名均在前 10%，那么计算平均权重作为该股票的基金持仓权重。下图展示了我们每季度选择出来的优选基金组合超配股的数量和平均流通市值。最近三年超配股数量在15只左右，平均流通市值显著提高，基本均在 1000 亿以上。平均持仓权重变化不大，在 11%左右。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def View_Overweight(hold_df:pd.DataFrame):\n",
    "    \n",
    "    hold_df = hold_df.copy()\n",
    "    \n",
    "    hold_df['period_end'] = pd.to_datetime(hold_df['period_end'])\n",
    "    hold_df['period_end'] = hold_df['period_end'].dt.strftime('%Y-%m')\n",
    "    \n",
    "    \n",
    "    df = hold_df.groupby('period_end',group_keys=False).apply(CreatOverWeightTable)\n",
    "    \n",
    "    df['mktcap_mean'].plot(figsize=(18, 8),\n",
    "                title='优选基金组合超配股的数量和平均流通市值',\n",
    "                marker='o',\n",
    "                color='#DC143C')\n",
    "                    \n",
    "    plt.xticks(rotation=90)\n",
    "    plt.ylabel('市值')\n",
    "    plt.legend(['优秀基金超配股平均流通市值'],loc='upper left')\n",
    "    plt.twinx()\n",
    "    df['num'].plot.bar(alpha=0.5, color='#696969')\n",
    "    plt.ylabel('数量')\n",
    "    plt.legend(['优秀基金超配股数量'],loc='upper right')\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "def GetOverWeightStock(df:pd.DataFrame)->list:\n",
    "    '''获取超配股票列表'''\n",
    "    \n",
    "    symbol_mean_weight = df.groupby('symbol')['proportion'].mean()\n",
    "    \n",
    "    # 降序\n",
    "    threshold = np.nanpercentile(symbol_mean_weight,90)\n",
    "    return symbol_mean_weight[symbol_mean_weight >= threshold].index.tolist()\n",
    "    \n",
    "def CreatOverWeightTable(df:pd.DataFrame)->pd.DataFrame:\n",
    "    \n",
    "    '''构建超配股票股票信息'''\n",
    "    \n",
    "    target_stocks = GetOverWeightStock(df)\n",
    "    \n",
    "    return pd.DataFrame({'num':len(target_stocks),\n",
    "                  'mktcap_mean':df.query('symbol == @target_stocks')['market_cap'].mean()},\n",
    "                index=[df.name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "hold_df['symbol'] = hold_df['symbol'].str.replace(r'\\D','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wpnOMxEtEHGiM2c9au9A/r0HPj8hFjbdMbA1eJUx0ChPwOz/WD/ESKvv7X9eH/XHcAvzFGHOxXy1yKjFLA+N9TeuAnJhrrfB7czzmj7GpD4EuNcaMb2J/a7wN7BWz0g3A7/xkTaRi4iC8ahOMt5rK/+Etc9sF73eXR2Je6+JNoboY7+s6D2/6T5Ux5k9401HCxpieeM13N+OvumOMSfErOVyaXgb4YrzE0Cds7ZfSlPU2Zslnv7JJREQkaXSW5AfAd2N/ofNLbx+y1j5jjJmP9wnPd5p9tYiIJLND8B6eT/O3vwDuNcaU4FVp/AXv4XIw3lKxt+D1uOjNtg/A+1trFxpjTjbe6h4RuXgrkcT29fiLtfbXfsJkpb8vC3jPj+v413WAb/wk/UBgMXA28D9rbdjvKfF32LavhzHmamttpHqxLLKqTWONpjgcgFdF8CtjzD/wEkAj8ZZ1TfHHEzEG7yH8Hbt1SdyxeCvivAssMsbU+a/5D/AaMBaY5z+M34Y3TWY1XhPOc/F6cfwMry/KT621r+E1BZ3qxz8x5vrj8R7kQ3jLBUeSQ0/hTZ8Z6/85CG8lnR9ba+cbY/7r37OhxpiDgasb3ZJ0P2bsVI1fGWNC1tp/+tfp2ug1/wXGWWvf8Y9P3OZGt4C1dmMzh0KANV7j9iq2VgMZvKqfhcaY8/G+Hk+wtZlvkX/OrXi9XA7B+51mGN69+TXe93Mvtm0AfBjwH39M2yzp7Cfz7m28P8YP8e5tqdl2yWeMMQuttbO383oREZEOwXFdd8dndUD+LySDrLW/MMb8G7jfWvuK/6lHBV7pa7HfWG5v//h3m48oIiKdmTGmi18hsEswxmRYaysCjJ8a23Mj0uuk8Rjwep206pcNY4zT1Gua278r2tW+n0VERNqqsyQ/huGV4XbF+2TqRLzu80/5/3WB/7PWfpag4YqIiIiIiIhIgiRt8kNEREREREREpCWScrUXEREREREREZGWUvJDRERERERERDq1pFrtpbCwvMVzdLKyMigpiX+ft6DiBhk72eIGGTvZ4gYZO9niBhk72eIGGTvZ4gYZO9niBhlbcYOPnWxxg4ydbHGDjJ1scYOMnWxxg4ydbHGDjJ1scYOM3RHi5uVlOjs+K3E6beVHamrjld46dtwgYydb3CBjJ1vcIGMnW9wgYydb3CBjJ1vcIGMnW9wgYytu8LGTLW6QsZMtbpCxky1ukLGTLW6QsZMtbpCxky1ukLGTLW4idNrkh4iIiIiIiIgIKPkhIiIiIiIiIp2ckh8iIiIiIiIi0qkp+SEiIiIiIiIinZqSHyIiIiIiIiLSqQW+1K0xpjfwFXCmtfatmP3HAncCDvBLa+2/gh6LiIiIiIiIiOx6Ak9+AHcAton9M4GTgBrgXWPMHGttuB3GkzDV1VVUVXl/SkqK2bBhAwUFGxg9+nhyc/MSPbyEc12X0tJSsrKydnhufX09ACkpbV96adOmUnr16r3dcTlOh16yWkREREREWmBXfybblZ9tAk1+GGOOASqAFY32DwQ2W2vX+dsrgH2AhUGOJ0hPP/0k/fr147jjjueBB/7AwQcfyvvvv8tFF11Gjx49+OijD3jkkYfo1q0bpaUl5OTksf/+B5CX1yf6IL9y5UqmTp3GunVrycvrQ1paGuD9A924cSP9+vXn2mtvZPDgPRtce+7c2Sxe/BVTplzd5Dfy5ZdfzpQp0+jZsxdXXfUzli1bytChw/j66+W8/PK/mTt3Ni+99AIFBRtIT09n+PARFBRsiL5+xYpvmD37DVJTUznxxGMZMmQo9fX1vPjiC8yYcTuLFn1BaWkJ119/M4cffgQAV1/9f2zaVApAeXkZdXV1ZGVlA5Cbm8uMGfdEx15fX88pp5zKokVf8o9/PMtNN93WYPx1dXVcc80ULrroMvbZZ18A5syZxZYtm5kw4afR84qKinjooZlMm3YD8+a9zqxZLxMOhwmFQqxduwbHcXjzzTe2uT933fUbzjjjbEaO3KfJr+2VV17OVVddS21tDRUVFTz00Eyuvno6AHvsMYguXbo0+ToREREREWk/8XgmW7NmNVOm3MbKlavi+kx2/fVTueaa6+nVqwuTJ0+K2zPZww8/zowZt7NkySKKijY2eCb7/e9/x5FHHs2iRV+wYcN6rrrqWqZOncK9997fYIxvv/0WM2feQ35+foMxr1u3lrvu+i177jkiPl+gBAss+WGM6QLcBIwD7mt0OB8oitku9PdtV1ZWBqmpLf+kPy8vc5t95S++Tum9T1JjV5JuBtL7ynPIHPe9FsdsKm59fT3vvjuPp556ii5dulBTU8GgQf3Iyvo+n3zyHj/+8Y855ZQTOOWUEwB45pln6NKlCz/60Y8aRc7k739/jltvvZXBgweTne0lC9avX8/atWu5+eabG5xdUVHBPfd4SYQhQwZy3XVXcvPNNzNo0KDoOeeccw5LlizhppuuZfLkyfztb09z7rnn8uSTT3LBBRew225ZnH/+2ZxwwnE8+OCDDBw4kEmTJjW4zplnnkleXiapqakMGzaU5557jtNPPx2AqqrNPPPMX3niiSfo1atb9N7ceustVFZWMmLECP71r39RWFjIxIkTmTNnDnvttVf0vMzMrtTX15OXl8nHHy/A2q+YMmVS9P3dddddDBs2jF//+nZeeOEFrF3I/Pnz+fLLLxk5ciQLFswHYOrUqRxyyCGceOLxLFz4H84+ewI/+clYbrzxRsrKyjj66AlMnDiRtLQ0vvxyAQ888ED0/RUVFXHrrTeSm5vb4L6deuqpWGvJy8uhSxf48ssvqK6uZsuWzSxZ8gUAe+89hKyszCa/L+IpqNjJFjfI2MkWN8jYyRY3yNjJFjfI2IobfOxkixtk7GSLG2TsZIsbZOxkixtk7I4cd9asWXEYyVZjxozZ7vHImOP1TJaXN4JnnnkmaZ7J8vIyqarazJNPPtngmWzx4sWsWbOCRx/9gi1btjBgwAA+++xDrF3EpZdOBGDatGmMGjWKXr26kZLikJ7eMD2QkhJqcI+TXZCVH9cDD1prNxtjGh9Lw+v1EeEA6TsKWFJS0eKL5+VlUlhY3mBf7ez5VF4zI7pds+hrCib9kvKyKtJOOXan47744t8xZiQ///ktLFu2lNWrV/HZZwtJS/Pe0l577ceNN04DYMuWLaxZswqAmTPvIzOzJwAzZtzD3nsPprCwnMrKGjZs2EhVVR0ARUXFVFbWRK9bU1PD7Nn/5MUXX+Dss8/jhBNO8q+zD1ddNZX8/L5MnHgRQ4YM5e67H+CWW6YzZco0cnJyefnlOQwePJTCwnKqqmopKtrMRx99yK9/fSt1dXWEQiGsXcZ119209b7V1lNYWE5qaipLly5jwoQzqa2tp7KykjVr1lJTE6KmJszy5asYMmQj6enpbNhQyp133s4f//hnysoq2bKlmsLCcn7/+z9wzz33U1hYziuvvMRTTz2O67qsWLGG1157g4ceepyMjAzy83sxZcpVFBSUsnLl23zyycf89KfnkZ6eDqRxxBHHcMYZZ/PQQ/cxatRRDBxoKCws5zvfOQaAu+66l08++ZiuXbuxfPlS0tK68IMfjOGJJ/7CyJEHc//9j0bf3+uvz6W4eCOnn35Wg69rYWE5M2b8lkGD9mS33QazYMEDlJQUU1hYxJw5c9lnn/2oq0ulsLC8ye+LeAkqdrLFDTJ2ssUNMnayxQ0ydrLFDTK24gYfO9niBhk72eIGGTvZ4gYZO9niBhm7o8etqKjZZl9GRnqT+1tie2OKHXO8nslyc3PJy8uM+zNZpPJj+PDBvPTS7Lg9k61eXciaNWvp1atXg2eynJz+XHvtTVx++cWMHn08Rx55DAUF65k8+SrGjDmNSZMmMnToPn7M7hx44KH07Nmz0f3tS1ZWVou/Lzp6kiTI5Md44FJjzANAL+CHxpizrLX/BjYAOTHn5gLr23rBqt8+Su3cdwCoSAlRX9+whYhbsLHJ11Ve9zuq7v5zk8fSvn80Xadd2Ow116xZzYMPzmTcuPFcccVUAC644GwefvhxUlO921tRsYVzzpnI559/RlpaOmlpaXTrlkF5eRmlpSWMGLFPNKsWsWDBO/6DPlRVVTFixMjosa+/XsaGDRu44YZb+PWvb+Xhhx+gpqaarKwcTj11LPn5fXFdl6+/Xs5jj/2RtWtXc/PN13P44Ufw+eefcf31t3DjjdMYNmyvaMxjjjkOx/HGUFlZwcyZd0e316//NnreoEGDue++h5k0aSJLly7lhBNOYuXKFey990jeeusNDjzwYPr3H8CQIUPp3r07X3zxefS1H3zwHvn5faNz6U49dSzp6enU19eTn9+XvLw+vPXWGyxbtpTbb/8l4BIKpbDPPvvx+eefsWHDer755msee+yPjBy5L59++gndumXw+9/fxUMPPcZbb73J3//+N049dWyzX6+Id955i8cf9xIgkWk5c+fOAWDcuPGMGXMa8+a9ztq1axk0yCtpW7duLVOmXA3Ali2beeWVl3Z4HRERERERCVYyPJOtWeM9kx1//Gg++OA/cXsm++ab5ZxwwkksX768wTNZly5dmTbtSs4882zKy8t44425rFmzmuOPP5GiosJowud///svjz76x2bv7W233cZ5501i//0P2OmvT0cRWPLDWhttoGCMeRx4HPiuMSYdeAXINcb0w2t4uhfweRNh4quuvpn9dTsd8r333uXMM8+mqqoK8BrIgMsdd/ySCy6YxIABu+M4IdatW0dWVrZfFbGBqqoq8vLyOOusc/nss09wnBArV65k4cLFbNiwnnHjxkd7ZKxf/y3vv/8uH3zwHrvt1o/hw0cwfLg37+rxx59m3rzXWblyBRMnXrTN+I488hjC4WoyMnoxevT3mDp1CjfccA0AVVXV0fM+/fR/nHPO+Tz44B845JDv8PnnC3n44ccB2Hff/UhJSaGqqooVK75h8mSvBKtnz54899wzVFRUUFpayvLly+jff0A05sUXX0ZWVjbr168DYI89BnL55VOavI8HH3xo9O9PPPEYNTU11NXVk56eRkpKCmefPRGA/v0H0Lfv/ey551Dq6uqoqalmw4b1pKenc+KJJ9GzZ0++/PJzUlJSGDXqKHJycqisrGDMmLGUlm6KXqO0tJSxY3/MqaeObVD5sWDBOyxd6vXnzcrK5tJLJ/P5558BEA7XU1zsJdAqKlpehSQiIiIiIsGJ5zPZmjWr+eqrorg/k+23n9db5PTTx7FgwQdxeybr0SOT5557Bqhj3bqC6DNZOBzmF7+4nffee5fvf/8HZGdnc+WVl/Pmm6/Tq1cvhg4dBsCBBx7MGWeczezZr7D77nuwZs0qHCdE//4DWLVqJeeccxYjRiR/4gPaZ7WXWPlAprXWNcZcCrzs77/CWrtzdVAxuk67MFql0VTZ1uaxlxNesmKb14XMYHq8eP9OXfP008/kk08+5sMP3wdg0aIv2Wuv4dTUbH073bp145hjjmPhwk959dU5jBy5L/n5ffnb357i9NPP4qijvCk3S5d+zv/+919Wr14V/eZLSUll1aqVrF27mo8//g8HHngwAwcO4ne/m8GiRV7fifLyMmpqanjnHa//xR57DOSWW34FwPz5bzJ69LH8858v0qtXb6qrq3jwQa/i4YorLm8wxvz8vkydOp358+c1eI9HHHE0JSXFhMNhDjjgQO68814mTZrIoEGDOPHEk9l994FkZHQnM9Mrc3rvvXd5/PE/RV9fVraJuro6Xn/91ei+iRMv4ogjjgK8hqalpaX07t2b5cuXUVtby9NPP01tbS1dunQFvKxrWdkmTj55DHfffSeXXTaFjz/+kG++Wc706TfRlHC4nvr6elzX9RsYua350nLAAQfxyScfR7crKip46aUXAKivr4tmS0VEREREJHHi+0xmWbTo07g/kx166GH8858vMnBgv7g+k+2++x6ceOLJDB48GEiLPpN9/vlC7rnnTsrKNjFv3ut0796d++9/hOnTr+LZZ5/mvPO2zm6oqqpk+fKllJVtoqioEHAoLS2hoGADlZWVcfxKJVa7JD+stRP9v74Vs+9N4NCmzg9Kl0kTGvT8iO6/+PS4XeO11/7N6NHfY+7c2YD3YJ+amsqAAbvTr19IPywmAAAgAElEQVR/nn/+GX7ykzMpLi4iOzuHyZMnsXjxIubOnU9NTQ0fffQBubl5VFdX89FH/yEtLZWePXux117DefPN1zBmOABTp05n7tzZHH/8ibzzzlusXLmC8867kEceeZALL7wkeu0vvlhIXl4OP/zhOA466BDuuuvXXHaZ941eVra1EqJPnz5Yu6jJioY1a1bzj388z6mnnkZ2dk6DYz/5yRlceeXl1NfXM3OmVy51xBFHRRMb0HxPjS+//ILXX5/Lhg3rGThwMPff/xKVlZV873snMm7cGF599Y1ogmHu3NmcfvpZVFdXU1NTzY507dqVESP2IS+vDx98sIA333wtmmCpaSbN5rouNTXVzS79lJGREZ1SU1lZwXvvvbvDcYiIiIiISPtqyzNZbW0tCxYsiPszWWZmT374w3EcdthhFBdvjPsz2dSpk6mpqY0+k+2//wH069ef88+/mE8++YgDDzwYx3E4+eQx3HnnHRx44MHR1/ftuxuHHno43bt3Z8mSxThOiGHD9vJ7H+4Wx69MYrV35UdCRZqaVj/yHOHlqwgN2YMuF5/e4manO7Jy5QpWrvyGK6+8JvoP7cMP3+fbb9fygx+cxsyZdzN06F707t2b3r17c999DwNw7rkTSElJYZ999uGee+6nV6/eADz33NNkZ+fwve99H4CSkpIG11u3bi2zZ78Sze699dYbbN5cTkqKtyLOwoWfcsQRR/Puu++Sl7ebX1HxQpNjt9ayceNG6urqGDRocHSpJ4DFixexxx4DsXYx/fsPoL6+HsdxcF2XhQs/pbraSxisXbs2WhbWEuFwPeeffzEjRngzpGLnkaWmprJ5czkZGRl8++06vv56GSNH7sPzz/+NQw45rNF9/4bU1FSGDt2Ll1/+BwUFG7jmmuvYe++R1NXVccUV10T/B9Grl1cR1LVr123Gc+edd7B0qWXq1OlNjjcUSiEvrw/g9fyQhmpnz6f64WcpW76a0JDd6TJpQtz+bYmIiIiItERbn8mGD9+bxx57jLo671E5Xs9kH374Prvvvgc1NTWBPZOFw26DZ7IJE87ippuuo1u3blx44SUUFRXy+ON/4uCDD2HmzLu54oprcByHjz76kBUrvgaIVn5EPnB+8803OfvsPbcZazLapZIf4CVAgnggc90wDz00kxtuuAWAwYOHMH36VYTDYW67bQYXX3weBxxwENOmTYu+5pe//DmrV69i1CivSiI3N5eZM2/jyy+99idFRUWkpaXywgvP+TH35Nprb6SoqIjp06+itraGgoIN9OzZi5qaGmpqasjOzuHCC8/huutu4osvFnLSST/ghhum87vf/Z5LLplIfX24QWXDhRdOomvXbpxxxlmMHTuejRuLeOSRB8nOzuacc04nFAoRDoe5664/MGfOLPbdd3+uuOIydt99D8aNG8fAgXsyc+YfKSgo4De/uZWLLrqMY48d3aJ7tu+++2+zb+3aNdxwwzQgzKhRRwPwxRcLGT/+DMrKNvH22/P4zW/uZvnyZQDk5eXzi1/cyLXX3shHH31IZmYml146mUceeQBrF/vz/ba67rrpHHLIUdFuzLGmT7+x2bFOnjyJHj168NhjDzfY/8Ybr3L88Se26P12Zo1XUgovWRHdVgJEREREZNcxevQJ2+wLclWdWPF4JsvOzuHhhx/go4/+C8TvmeySS37G448/yoQJE6iuro3bM9kFF/yUIUOG8eSTT/LVV19Hn8n69x/AY489wqWXTqZHj0yefvpJ/vOfD7j00skcdtgR3H77L3jttX9TU1PNf//7UbP39JNPPiE7+xVOOeXUuH+92pvT+OGwIyssLG/xYDvaElDhcJhQKLTdc+I95sgySfn5veIWt76+PprFzMxMo7y8NnosHA4TDoejHZXbYmfvRaSkLd5xd2RXXyYtiH46TUmGe9EecYOMnWxxg4ydbHGDjK24wcdOtrhBxk62uEHGTra4QcZOtrhBxk62uEHGbm3cljyT7Uzc7Yk8k0WuG4/Ysc9k1dVVdOnSNRo38kzmui4pKSkN3m9tbS1paWkA0Q+Jm5vyH9Ga8eblZW4/WILtcpUfidKSf2TxFo8kRGORf2Tg9dWITX7E/qNOlCDes+xYePmqVu0XEREREWlvnfGZLLJARMT2nskiiQ/YcdKjM9KTooi0WWjIHk1XfgzZo/0HIyIiEpB5815rcn9GRjoVFQ07qjdV+i8iIomT2I/pRaRT6DJpQtP747iSkoiIiIiIyM5S5YeItFmkqWnljfdAdQ1OTm+6Xn+Jmp2KiIiIiEiHoMoPEYmLtFOOxenZA4DUYw5V4kNERERERDoMJT9EJC5c18UtKQMgXLAxwaMRERERERHZSskPEYmPzRVQVweAq+SHiIiIiIh0IEp+iEhcuMWbon8Pb1DyQ0REREREOg4lP0QkLsKlZVs3yjbjVlUnbjAiIiIiIiIxtNqLiMRFbOUHgLthI87AfgkajYiIiIiIdCTGmKOB2/GKMFxgInCW/2cjUGCt/VFQ11fyQ0Tiwi3xkh8pu+VR/20h4YKNhJT8EBERERERjwVOs9aWGGN+CkwHKoAp1trXgr64kh8iEheR5EeXkUOo+LYQd0NRgkckIiIiIiIdhbW2AMAY4wD7AzVANtAuDw7q+SEiceEWez0/0vcZBqjpqYiIiIiINGSMuQyvAuRIvCkwLnCfMeY9Y8yUIK/tuK4bZPy4qqurd1NTUxI9DBERERHZBc2aNavF544ZMybAkbRcUGNOxnshIoFzWnqiMeZnQJq19l5/uzvwBjDVWrsgiMEl1bSXkpKKFp+bl5dJYWF53McQVNwgYydb3CBjJ1vcIGPHO27FZbdQN/8jdn//r6we9VNSTzyKjHtviFt8SJ57EXTcIGMnW9wgYydb3CBjK27wsZMtbpCxO3LcioqaJvdnZKRvcywe7yGoMTc1XmjdmJPxXrRn3CBjJ1vcIGMnW9wgY3eEuHl5ma0J/SLwKnAvgLV2izFmPjACCCT5oWkvIhIXbkkZpKaSNrg/pIRwCzTtRUREREREPMaYQTGb3wW+Mcb08Y+lAIcDC4O6flJVfohIxxUu2YST3QsnJQUnL5uwkh8iIiIiIrLVWcaYM4DNQBUwCXjAGLM73pSZl6y1HwZ1cSU/RCQu3JIyQv3zAQj1yaF+0XLccBgnpAIzEREREZFdnbX2DuCORrvHt9f19VQiIm3m1tTC5gqcrF4AOPk5UFfnTYURERERERFJMCU/RKTN3JJNADjZXvIj1CfX26/lbkVEREREpANQ8kNE2ixS4eH07un9Nz8HgHBBUcLGJCIiIiIiEqHkh4i0mVvsVX6E/MoPp4+X/FDlh4iIiIiIdARKfohIm7mlfuVHllf5EYpWfij5ISIiIiIiiafkh4i0WaTyo0HDU1T5ISIiIiIiHYOWuhWRNgtHGp5GKj/yVPkhIiLbN2/ea03uz8hIp6KiZpv9o0efEPSQRESkE1Plh4i0WbThaaTnR/du0CMDd4ManoqIiIiISOIp+SEibdZ42gtAqE8ObkFxooYkIiIiIiISpeSHiLRZtOFpr8zoPic/B3dTOW5VdaKGJSIiIiIiAij5ISJx4BZvgp49cNK2thEK5ed6x1T9ISIiIiIiCabkh4i0mVuyiZDf7DTC6RNpeqq+HyIiIiIiklhKfohIm7jhMG5pWbTZaURIy92KiIiIiEgHoeSHiLRN2RaoDzdodgoxlR9KfoiIiIiISIIp+SEibRIujaz00nDaS7Tyo0DJDxERERERSSwlP0SkTZpa5hbA8Ruehjeo54eIiIiIiCSWkh8i0iZuib/MbePkR3YvSAmp8kNERERERBJOyQ8RaRO3xK/8aNTw1ElJwcnNIqylbkVEREREJMGU/BCRNolOe+ndc5tjTp8c3IKNuK7b3sMSERERERGJUvJDRNokMu0l1KjyAyCUnwu1ddFzREREREREEkHJDxFpk+amvcDW5W5dNT0VEREREZEEUvJDRNok2vC0iWkvkeVuw2p6KiIiIiIiCaTkh4i0SbhkE3RJh4yu2xxz8iOVH0p+iIiIiIhI4ij5ISJt4hZvwsnqieM42xwL+dNewkp+iIiIiIhIAin5ISJt4paU4WRt2+8DwMnP9c4pUM8PERERERFJHCU/RGSnuVXVUFnVZLNTgFCfbECVHyIiIiIiklhKfojITttes1MAp3sGdO+Gq4anIiIiIiKSQEp+iMhOiyxzG2qm8gMglJ+LW1DcXkMSERERERHZhpIfIrLTopUfWU1XfgA4fXJwS8twq2vaa1giIiIiIiINKPkhIjvNLfYqP5preAoQiix3q6kvIiIiIiKSIEp+iMhOC5dEkh/br/wANT0VEREREZHEUfJDRHZapOdHc6u9gCo/REREREQk8VKDDG6MORq4HS/J4gITrbXLY46/AXQH6oC51trbghyPiMSXWxzp+dF88kOVHyIiIiIikmiBJj8AC5xmrS0xxvwUmA5MijmeDXzHWlsb8DhEJABuaUt6fuR65xYUtcuYREREREREGgs0+WGtLQAwxjjA/kDj5R5SlfgQSV5u8SZwHJxePZo9x8lX5YeIiIiIiCSW47puoBcwxlwGXAUUAuOttd/GHLNAZPs2a+0b24tVV1fvpqamBDZWEWmdVUecTf3GUgbbWc2e49bV8XX/4+l6yEj6/+uBdhydiIh0ZLNmNf+zoyljxowJaCQt15oxd4TxQnBjTsZ7ISKBcxI9gO0JetoL1toHgQeNMT8DJgD3xhwzAMaYwcDbxhhjra1oLlZJSbOHtpGXl0lhYflOj7u94wYZO9niBhk72eIGGTseceuKSnCyejWI01RcJzeL6rUFbb5eR74X7Rk3yNjJFjfI2MkWN8jYiht87GSLG4/YFRWNC4I9GRnpTR7rCD9DWjPmeNz3oMYcj3ucjPeiPeMGGTvZ4gYZO9niBhm7I8TNy8uM+/XjqT1Xe3kRuKipA9bab4B1QP92HI+ItIFbX49bWr7dZW4jnPwc3IKNBF1pJiIiIiIi0pRAkx/GmEExm98FvjHG3GqM+aExpqsxpqd/Xh7QB1gV5HhEJH7css3gutttdhoR6pMDtXW4pWXtMDIREREREZGGgp72cpYx5gxgM1CFt9LLNCDT/zPbGFMLpACTrbXVAY9HROLELd7xSi8Rkaan7oaN0ILzRURERERE4ino1V7uAO5otPuSmL8fGuT1RSQ4bolXxdGSaS+hPltXfEkZvmeg4xIREREREWmsPXt+iEgn4pb4lR/ZLan8yPVeU6DlbkVEREREpP0p+SEiOyU67aV3ayo/igIdk4iIiIiISFOU/BCRnRKZ9hJqUeWH3/NDlR8iIiIiIpIASn6IyE5pzbSXUGzDUxERERERkXam5IeI7JRow9MWTHtxumdARjfCqvwQEREREZEEUPJDRHZKOFL50YLVXsCr/lDlh4iIiIiIJIKSHyKyU9ziTdCtC063ri063+mTg1tahltTG/DIREREREREGlLyQ0R2iltShpO1434fESE1PRURERERkQRJTfQARCT5uK6LW7KJ0LCBLX6NE13udiOhAX2DGpqIiIiIiHRAxpijgdvxijBcYCJQAfwV6A7MstbeFtT1VfkhIq1XWQ3VNS1qdhoRys8FwC0oCmpUIiIiIiLScVngNGvtUcBDwHTgVmAmcDhwgjFmZFAXV/JDRFotssxtqAXL3EY4+VsrP0REREREZNdirS2w1pYYYxxgf6AGOB6Yba11gdn+diAc13WDih13dXX1bmpqSqKHISIiIiJtNGvWrFadP2bMmIBG0nKtGXNHGC8EN+ZkvBciEjhnRycYYy4DrgIKgfHA/6y1ff1jFwJ7WmtvDGJwSdXzo6SkosXn5uVlUlhYHvcxBBU3yNjJFjfI2MkWN8jYbYlb+/ZHVF56C12uPI8ukya0KG54QxGbR59L6snHkPG769p9zJ0pbpCxky1ukLGTLW6QsRU3+NjJFjcesSsqaprcn5GR3uSxtr6PeNyL1ow5Hvc9qDHH4x4n471oz7hBxk62uEHGTra4QcbuCHHz8jJ3eI619kHgQWPMz4AJQHrMYafRdlxp2ouItJpbUgbQqtVenJwsCIVwNe1FRERERGRX9yJwEbDZGNPV35cLrA/qgkp+iEirRXp+OFktb3jqpKbg5PQmrKVuRURERER2OcaYQTGb3wW+Ad4BTvb7gJwCzA/q+kk17UVEOga32E9+tKLhKXhNT8NLVuC6Lo6zwymBIiIiIiLSeZxljDkD2AxUAZOASuAp4DpgjrX246AuruSHiLSaW9r6aS8AoT45hL9YirupvFXL5IqIiIiISHKz1t4B3NHEodHtcX1NexGRVotUfoRamfyILHervh8iIiIiItKelPwQkVZzSzZBSgh6dm/V60J9cgFv5RcREREREZH2ouSHiLSaW1KG07snTqh1/wuJVn6o6amIiIiIiLQjJT9EpNXCxZta3e8DvJ4foGkvIiIiIiLSvpT8EJFWcevqoWxzq5a5jYhUfmi5WxERERERaU9KfohIq+zsSi8AoXyv54cqP0REREREpD0p+SEireKWeCu97FTlR48MyOhGWMkPERERERFpR0p+iEiruCV+5Ud26ys/AEJ9snELtNqLiIiIiIi0HyU/RKRV3GK/8qN36ys/wOv74ZaU4dbUxnNYIiIiIiIizVLyQ0RaJdLzI7TTlR/+ii+FxXEbk3RctbPns3ns5Szvexybx15O7ez5iR6SiIiIiOyCUhM9ABFJLtHKj51oeArg+E1PwxuKCPXPj9u4pOOpnT2fymtmRLfDS1ZEt9NOOTZRwxIRERGRXZAqP0SkVaI9P3ai4SnEVH6o6WmnV/3ws03vf+S5dh6JiIiIiOzqlPwQkVYJR1Z72clpL06+l/wIFyj50dmFl69q1X4RERERkaAo+SEirdLmaS+q/NhlhIbs0ar9IiIiIiJBUfJDRFrFLSmD7t1w0tN26vWhSM8PVX50el0mTWh6/8Wnt/NIRERERGRXp+SHiLSKW7Jpp6e8ADi5WRAK4W4oiuOopCNKO+VY0n/204b7zhurZqciIiIi0u6U/BCRFnNd10t+9N65ZqcATmoKTk5vVX7sIkJ52QD0uujHALjrlfQSERERkfanpW5FpOW2VEJtHaE2VH4AOH2yCS9dieu6OI4Tp8FJRxRethKAHuNPoPyND6mb/xHulgqc7hkJHpmIdGbz5r22zb6MjHQqKmq22T969AntMaTtamq80LHHLCKSbFT5ISIt1tZmpxGhPrlQU4u7qTwew5IOrH6Zt7JLuhlM6snHQFU1dfM+TPCoRERERGRXo+SHiLRYdJnbNiY/Isvdupr60umFl63E2S2PUI8M0k4+BoDaOW8neFQiIiIisqtR8kNEWsyNJj92vucHQEjL3e4S3NJy3KISUoYOBCBlyB6EzJ7UvfNfVf2IiIiISLtS8kNEWswtKQPiV/mhpqedW73f7yPkJz8A0k45BurqqH39/UQNS0RERER2QUp+iEiLRXt+tLHhaShflR+7gnA0+bFHdN/WqS/zEzImEREREdk1KfkhIi3mlkYqP9o27cXpkwtAWMmPTi3S7DQlpvIjNKAvKfsZ6j/4jPDG0kQNTURERER2MUp+iEiLRSo/Qm1d7SXa8LSozWOSjiu83K/82HP3BvtTTzkWwmHqXn03EcMSERGRdlI7ez6bx17O8r7HsXns5dTOVuWnJI6SHyLSYvFqeEqPDOjWVZUfnVx42Sqc/vk43bs12J920tHgONT+S78AiYiIdFa1s+dTec0MwktWQH094SUrqLxmhhIgkjBKfohIi7klZZCaCpnd2xTHcRxC+Tla6rYTC5dswt1YSkpMv4+IUJ8cUg7dl/pPviT8bWECRiciIiJBq3742ab3P/JcO49ExKPkh4i0WLh4E05WTxzHaXMsp08ObvEm3JraOIxMOpqw3+8jdqWXWNHGp/9+p93GJCIiIu0nvHxVq/aLBE3JDxFpMbe0rO1TXnyhPn7fj8LiuMSTjiXcxDK3sVJPPBJSQlr1RUREpJMKDdm2+nN7+0WClproAYhIcnBraqF8C86IoXGJ5/hNT8MFGwn1z49LTOk46pdHVnpp5hefrF6kjDqQ+nf/S3jlOkID+7Xn8EQ6pXnzXttmX0ZGOhUVNdvsHz36hPYYkojswrpMmkDlNTO23X/x6QkYjYgqP0SkheK1zG2EE6n8UNPTTim8bBU4zjYrvcRKO+VYAGrnvN1ewxIREZF2knrkQQ22Q2Yw3e6aHv35L9LelPwQkRZxS/zkR3bblrmNCMVUfkjnE162EmdAPk63rs2ek3b8KEhL1dQXERGRTqj+q+VbN1JS6P73PyjxIQml5IeItIhb7C9z2ztOlR/5uV5cVX50OuHiTbjFm0hppt9HhJPZndRjDiW8dCX1S1e0z+BERESkXdQv8pMf3bpAfT3uxtLEDkh2eUp+iEiLuKVe8iMUr8qPPpHKj6K4xJOOI9rstAUNzdJO8Vd90dQXERGRTqX+q2UApI46EAB3vX7nk8RS8kNEWsQtjvT8iE/yw8nNAsdR5UcntKOVXmKlHnsYdOtC3ey3cV036KGJiIhIOwkvWg6Z3Uk5eB9vW1OdJcGU/BCRFglHpr3Eq+FpWipOTm/CSn50OvXLIiu97Dj54WR0JXX04YRXrSO8aFnQQxMREZF24G6pILxiLSl7DyHU15/qrMoPSbBAl7o1xhwN3I6XZHGBidba5THHjwXuBBzgl9bafwU5HhHZeZFpL/FqeArecrfhZatwXRfHceIWVxIrvGwlhEKE9hzQovPTTj6GutnzqZ39NikjhwU8OhEREQla/eJvwHVJ2XsIjprcSwcRdOWHBU6z1h4FPARMb3R8JjAOOAX4nTFGlSgiHVS04Wmcpr2A3/ejugY2bY5bTEks13UJL1tFaEBfnK5dWvSa1KMPgczu1M55GzccDniEIiIiErR6v5ozZcRQQtEm96r8kMQKNNlgrS2w1pYYYxxgf6AmcswYMxDYbK1dZ60tAlYA+wQ5HhHZeW6p3/MjTqu9ADiRpqeF+iSgs3A3luKWlhEauuNmpxFOehppx4/CXV9I/adfBTg6ERERaQ9hf5nb0IghOHnZ3j5NdZYEC3TaC4Ax5jLgKqAQGB9zKB+ITf8V+vualZWVQWpqSouvnZeX2fKBtkJQcYOMnWxxg4ydbHGDjN2auFVlm3F79aBPv6y4xS0Z3I9ioGdVBRktfE1HuBcdIW6QsdsSt2LxEjYDPfYbRk6jONuLW3HmSXz70uukznufvO8f3urrdsR7kYi4QcZW3OBjtzVuRkZ6i/fH6z20JU5z423uWGuuFdS9aM2Yg4obr9gd9V5sT0f9t5eI2B097uolX+NkdCX/0OE4KSlU5GURKioOZNwd/V60Z+xki9veAk9+WGsfBB40xvwMmADc6x9Kw+v1EeEAzf9fFCgpqWjxdfPyMiksLG/dYBMYN8jYyRY3yNjJFjfI2K2NW1tYgtOr5w5f05q4NT16AFCyZA1b9h2xw/M7yr1IdNwgY7c1bs3HiwGo7te3QZwdxXWH74WT1ZPyl97EveJ8nFYmujvivWjvuEHGVtzgY8cjbkVFzTb7MjLSm9wfj/fQ1jE3NS6Iz5iDuhetGXNQceMRuyPfi+Z05H977R27o8d1q6qpsStI2c9QVOw9v6XslkfNkpUUFJTFtc9bR78X7Rm7I8Tt6EmS9uyx8SJwUcz2BiAnZjsXWN+O4xGRFnJdF7e0LK7NToGtc0ALNAe0s6hvxTK3sZy0VFJPPAp3Yyn1H30exNBERESkHYSXrID6MKG9h0b3pe6WB1XVUKY+b5I4gSY/jDGDYja/C3xjjLnVGPNDYDmQa4zpZ4zJBfYC9BuvSEdUthnq6uO2zG1EtOeH5oB2GuFlq7yVXga3bKWXWGmnHAtA7Zz58R6WiIiItJOtzU6HRPel9ssDIKymp5JAQVd+nGWMWWiMeQ+4AK/3Rz6Qaa11gUuBl4E5wBXW2qbr50QkocIlfrPTOK70AhDylz5ztfRZp+C6LvXLVhLaYzecLtudxdiklING4PTJofbVBbg1tQGMUERERIJW7zc7TRkRU/nR10t+uPrASxIo0J4f1to7gDsa7b4k5vibwKFBjkFE2s4tiSxzG9/KDzK7Q7cuqvzoJNyiEijbTOg7++3U652UFNJOOpqaJ16i7v3/kXbsd+I8QhEREQla/aJlkJZKaMjWld9SVPkhHUB79vwQkSTlBlT54TgOoT45qvzoJMJLI/0+Wr7MbWORqS91szX1RUREJNm4NbWEl6wgZAbjpG39nD11N1V+SOIp+SEiOxSt/Ihzw1MAJz8Xd2Oppjl0AvXLveRHSiubncYK7bsXzoC+1L7xAW5VdbyGJiIiIu0gvHwV1NaREtPsFNTzQzoGJT9EZIfc4oCmvQBOXrZ3jcLiuMeW9hVetgpoW+WH4ziknXwMVFRS9/ZH8Rpah1A7ez6bx17O8r7HsXns5dSqukVERDqZ+kWRfh9DGuxX5Yd0BIH2/BCRziEy7SUU52kvsLXpabiwmFD//LjHl/YTXrYSUkKEBrV+pZdYaaccS80jz1E7+23STjwqTqNLrNrZ86m8ZkZ0O7xkRXQ7MtVHREQk2dV/FVnppWHlR6hHBvTIIKypzrs0Y8wAYCbQBwgDPwXOA84CNgIF1tofBXV9VX6IyA4F1vAUcCIrvuiTgKTmrfSyitDA/jjpaW2KFdprEKE9d6du/n9wt1TEaYSJVf3ws03vf+S5dh6JiIhIcMKLlnsfhOw1aJtjoT45uOs17WUXtwWYYa09EngKuAbIAaZYa48KMvEBSn6ISAtEG54G0PMj1CcXgHCBfhgmM7dgI5RvadOUlwjHcbxqiOoa6t78MA6jSxzXdan7bHG0GWxj4eWr2nlEIiIiwXDr66m3X5ZjyvkAACAASURBVBMaOrDJJe+d/BzcTeXq6bULs9aWWGs/8DfXAb2BbKBdHgSU/BCRHQqXbIL0NMjoFvfYqvzoHKL9PobsfLPTWKknHwNA7Zzk7Ivhlm2m5ulX2PKjyVSceTW4bpPnxS4DKCIikszCK9ZCZTUpew9p8ngo3/vAS7/zie/HwCuAC9xnjHnPGDMlyAs6bjO/kHVEdXX1bmpqSqKHISIiItLhzJo1q8XnjhkzJsCRtExrxgutG3NQ96IjxA0ydkeIKyJJzWnJScaYU4CrgROsta6/rzvwBjDVWrsgiMElVcPTkpKWz/3Oy8uksLA87mMIKm6QsZMtbpCxky1ukLFbE7fskB8T2mM3evzjvrjGBXBr6yg/4DRSDtmH7n+Zsd1zO8K96Ahxg4y9s3Erb7qX2hdepfvLD5IybNvqj52JW/3o81T/7s90vXUK6eNPivuYd6Slcd1N5dS+Mo+a5+dEp7c4u/clffxJpI07gVBuFuA1Pa3+49+8c7p1odttV8a92Wmi70Vnjxtk7HjEraio2WZfRkZ6k/vj8R7aOuamxgXxGXNQ96I1Yw4qbjxid+R70ZyO/G+vvWN31LhVMx6h5i8vkvHUb0k9aOQ2sdfO/BtVt95PtxnTSDt1dFuHG43bEe9FImJ3hLh5eZk7PMcYsyfwW+B7kcQHgLV2izFmPjACUPJDRNqfW10DFZWBNDsFcNJScbJ7qwTy/9m78zg5yjrx45+nqrp7ZnJOJskkISF3ilwQjlyAOQgIRBRUjvVCVEBFVzxQdFfdFV0VBVdFdldgVxQPDpUfCAE5EsIRIiThSDJJJTO572tyzdHdVfX8/qjunkky93R39cx8369XXpCa7ud5ksxM93zre3RxfuU2sEyMkcOytmbksjnE7/4NyUUvtxj8CIPWGu/tdSQfe5bkMy9DPAGWiXXphUSvuRxz1lko48TK0sjCuUQWzqX+6i+S2LwTq5tMshFCCCEAvHVVoBSmPabJj6ty6fPW09m23Rt4BPi04zi7U9cGO46zz7ZtE5gF/DVX+0vwQwjRolw2O01T5WX4m7ajtUapNmXLiQKitcar2oZx+rBOT3ppzDitHHPaRLw33g1GIQ8akLW1O0ofOUbiycUkH3s2GO0LqBFDiV57GZErL85kebQkdvZEEhWb8Ku2Ydqjc31kIYQQIue07+NVVGKMHo7q1XSPuEzPjz1yw6sH+yIwGrjbtm2AOFBt2/YIgpKZ/+c4Ts663UvwQwjRIn0oGHNr9M9d8MMoL8OvqISjx6Ff6+lyorDoPQfgeC3G+edkfW3r8jl4b6/Dfe41oh97f9bXb0py0VLi9z3C0artGGNHEL3pWowhg0g+9gzJv7+ayvKwsC6fQ/SayzBnnHlKlkdLYmdP5NgfnsZb7UjwQwghRLegd+wN3gvMnd7sY9JN7iXzo+dyHOfHwI/D2l+CH0KIFunDQfBDDchN2QuASt3R9/cdxJTgR5eTzoDIxpjbk0Uuew/xH99H8pmleQl+JBctpe62ht4z/oYt1H/9J5nfGyOHEbnmciJXXYzRwWyoorMnAuCt3ggFVs4jhBBCdIRXUQmAOXFcs49RpX0hYuHvkeCHCIcEP4QQLUpnfqjS3GZ+AOh9h2D8qJztI3LDS425NcdlZ8xtY8agAZgzpuL94138Xfswhg3O+h6Nxe97pOkP9OlFyT3fwZw+tdOlWdGJYyAWxVvtdGodIYQQolBkgh+Tmh5zC6AMAzW4DL1Pyl5EONqepyuE6JH86twHP9TgVAOsvXInoCtqyPzIfvADIHJ5MBEl+ewrOVm/MT8VyDlFXT3WjDOz0pNGRSzMiWPxN25B18c7vZ4QQggRNm9dFQDmxOaDHxD0/dAHqtGul49jCXECCX4IIVqUj4anDZkfciegK/Iqt4JlZXXSS2PWJeeDZZJ8ZmlO1k9Lvvxmsx8zxma3pMecMh48P/NmUQghhOiqtNb4FZWo4UNQrZQvq/Iy8Hz0weo8nU6IBhL8EEK0KFP20j+HPT/SDbBk3G2Xo7XGr9qOMeo0VCQ3lZRGaT+s2Wfjr63E27Iz6+trrYn/+hHqPv/v0ExmR+yma7O6pznVBsBbsyGr6wohhBD5pvccQFcfbbHkJc0YnLrhJe/5RAgk+CGEaJE+nIfMj8GS+dFV6V37oLYuJ81OG7MWBqUv7rMvZ3VdXVNH3Vd+SPwXv0WVl9Hrj3dTfNftGPZosEwMezTFd91OJLV/thhTJwDgvSvBDyGEEF1bW5qdpqkhUuoswiMNT4UQLdKHjoBSraYxdkrf3lAUk8yPLiiXzU4bi1w0i/pohOSil4l97iNZWdPfuovaf/4+fuVWzPOmUPyzb2EMLMWcOoHIwrkMGtSH/fuPZWWvkxkjh0Hf3viS+SGEECdYsuT5Jq+XlESprU2ccn3+/EtyfSTRCq8i1e9DMj9EgZPMDyFEi/ShI6i+vVGWmbM9lFIYg8vkhbALyuWY28ZUn15Yc6bjV27F27il0+slX36T49d9Gb9yK9GPfYCS//0hxsDSzh+0jZRSmFPG42/dhT6SmwCLEEIIkQ/+uiDzw2il2SlI5ocIlwQ/hBAt0oeP5rTkJU2Vl6EPHUYn3ZzvJbLHrwoyP3I16aWxyOVzAEgu6njj0xP6e9THKfrhVyn618/lrF9JS8wpqdKXtRvzvrcQQgiRLV5FFaq8rE03ESTzQ4RJgh9CiGZp30cfPoYqzV2z0zQ1uAy0Ru8/lPO9RPZ4VdsgYmGcnptJL41Z82ZAcRHJRS+jtW7380/p7/H7nxK96uIcnLRtTOn7IYQQoovz9x9C7zuIOan1fh8AatAAUEoyP0QoJPghhGiWPnocfB9VmvvMj/SdAF+annYZ2vfxK7dhjB6e07KoNFVchHXRLPT23fjtzJbwtuyk5iNfxX3uNczzptDrsV9mMi/Ckgl+rJbghxBCiK4pPbK9LSUvACoaQQ3oL5kfIhTS8FQI0azMmNt8ZH6Uy8SXrkbv3g919XkpeUmLLJyL+/RLJBe93ObgRfLlN6n7+k/gWA3Rj32A2DduDKXM5WTG4DJUeRneagetNaqZMbtCdIS3cQveyrXEq4+iSvtinjsZc/yosI8lhOhm/Eyz07ZlfgCoIWX4ldvktU/knWR+CCGapatTY27zmfkhdwK6DC/V7NTMcbPTxqwLzoG+vUk++zLa91t87An9PeKJUPt7NMecMgF9oFrugIms8jZuwX3uNfTBw+D76IOHcZ97LSvNgoUQrUsuWsrxq26hasg8jl91S6d6VRU6L9XstD3BD2PwQIgn4MjxXB1LiCZJ8EMI0Sxdncr8yFPDU5DMj66kYdJL/jI/VDRC5OLz0XsO4L21rtnH6Zpa6r78HwXT36M5mdIXGXkrsshbubbp66sq8nwSIXqe5KKl1N12J/6GLeB5+Bu2UHfbnd02AOJVVKEG9Mu8j2sLNSR9w0v6foj8kuCHEKJZ+Sx7ke7fXY9fmZ70kr/MD4DIwtTUl2eafiPpbdlJzT99Fff5ZZjTpxZEf4/mZCa+vOuEfBLRHeh4AnfV2iDjo6mPp76nCyFyJ37fI01fv//RPJ8k9/SRY+idezEmjWtX+YoxWMbdinAUTu6vEKLgpMtejDyUvahBAwBpeNqVeJVbIRrBGDE0r/uaM85CDeiH+/dX0d/87AkfO6G/x8c/QOzrhdHfoznmlPEAeGtk3K3oOH/HHhIPPUHi3RWQTIJS0MREpHxk8QnR06VHwLf1eleWbnZqtrHZaZpk+4qwSOaHEKJZmbKXfDQ8jUZQZf3RchegS9C+j79pO8boESgz95NeGlOWiXXpheiDh/HeeDc4z8n9PX70NYr+pbD6ezRF9e2NMeo0vDUbWu1hIsTJ3HfWU/uVH3L8shtJPPQExCKYs6cFY6GbYJ4zKc8nFKLnMUae1vT1sfnNkswHrwPNTgGM8lTmxx55zyfyq7DfFQohQtUQ/MjP3UI1uAx/yw7p/t0F6J17oS6e95KXtMjlc0n+6Wlqv/ojqj77HSguguO1qCGDKLnn25iTx4dyro4wp07A/9sS/K27MEcPD/s4osBpz8Nd8g8SD/4108PDOGMM0Rs+RLQk0RCMjFh4qyrQB6pBKayLZ8u0FyHyQI0cBpu2n3I9dtO1IZwmtxqanUrmh+gaJPghhGiWn572kqdUaaO8DH9dFRyrgb6987Kn6Bgv1e/DzGOz08b8PfuD/zma6hR/vBaA6Gev61KBDwBzqk3yb0vwVjsS/BDN0rX1JB9/nvjv/h96+24ArDnTid7wQcyZZ6GUQi15PvN4c/wozPGj0IuXkVi3GWPIoLCOLkSPoY8cCzIS+/TCGDIoKHXxfcy5M4gsnBv28bLOr6iCPr1Qw4e063lGKvghmR8i3yT4IYRolj50BIpiqOKivOynGo27NSX4UdAaJr2Ek/mReOCxJq8nH36a2HUL83yazjFSE1/81RvhAwtCPo0oNP7+QyR+/ySJRxYFwb5ohMjVlxK9/oNtGjNtlpUCm/EPHZbvq0LkWOJPT0FNHbHbPkPs0x+mrE+ULTM+grdsFf7OvRinlYd9xKzRNbX4W3Zizpja7mxd1asEepdI5ofIO+n5IYRolq4+kpd+H2mZiS/yYljw0sGP0DI/ulFDOfOMMWCZeKtl4oto4DmbqfuXn3F8wQ0k7n8UZRpEb/kovV/8LcV33NqmwAeAObA/IJNehMg1XVtP4ndPQN/eRK+7HACjKEbs1k9C0iX+y9+FfMLs8tZvBq0xJ7av30eaUT5QMj9E3knmhxCiWbr6KMaYEXnbL10DKhNfCp9XuQ1iUdTwcO5iGWNPx9+wpcnrXY2KRTEmjMZbV4VOJFHRSNhHEnmUXLSU+H2PcLRqO8bYEVhzpuNVVOItewsAY/Rwop/8IJEPXIQqirV7fXNgKQD6UNPjb4UQ2ZH487Pow0eJ3vLRILMhJXLFPBIP/pXk35YQ/eSH2t0fo1B5FR3r95Gmysugahu6rj5vGcZCSOaHEKJJuq4e6uN5HY2YyfzYK8GPQqY9L5j0Mib/k17SYjdf1/T1LtpQzpw6AZIu/sYtYR9F5FFy0VLqbrszCOR5Hv6GLSQeeAxv2VuYM86k+L/+jV5/+x+i117eocAHgNG/D1gm+tDR7B5eCJGhE0kSv/kLFBcR/fgHTviYMgyKbvsMAPV3/S+6iTHUXZGfGnNrtHPSS1pm4ou85xN5JMEPIUST0inSRh7LXiTzo2vQO/ZCPNHmtPtciCycS/Fdt2PYo8EyMezRFN91e5dtKGdOCfp+eKs3hHwSkU/x+x5p8roxchi9HvwxkXkzUUYn36ophSrth64+IuOUhciR5JMvovceJHrdQoz+p75vss4/G/PCc/GWv4336soQTph9XkUlFMcwRg7r0PNl4osIgwQ/hBBN0odTk17yNOYWwBgc3AWQzI/C5mWanYbT7yMtsnAuvR+/l7G7X6L34/d22cAHgHmmBD96omZ71+zcm9V91IB+4Hro9HQkIUTWaNcjfv9jELGI3vDBZh9X9LVPg1LU3/1/aM/L4wmzT9fH8au2YZ4xpsMZoJnMD+n7IfJIgh+iR0guWsrxq26hasg8jl91C8lFS8M+UsFLZ37kM/hBv94Qi+LvkxfCQuanxtyGHfzoTowxI6C4SIIfPYj2fSgpbvJj2e5dky5flKanQmSf+9yr6O27iXzwkkz5blNMezSRKxfgb9hC8okX83jC7AtK9XyMDjY7Bcn8EOGQ4Ifo9pqqqa677U4JgLTCr04HP/JY9qIUanCZZH4UOC8z6aXrNRctVMo0MSePw6/ahq6pDfs4Ise01sR/dB8cq2ny49nuXaMGpCe+SNNTIbJJax2UrxkGsc9c3erjY1+6HmJR4vc8FPRW66I62+wUJNtXhEOCH6LduloWRXM11fH7H83zSboWXZ0qe8ljw1MAo7wMffAwOunmdV/Rdn7lNiiOoU4LZ9JLd2VOmQBa41VUhX0UkWOJXz9C4g9PYowfSdEdX8p57xpDMj+EyAl36Rv4G7YQWTgXY8TQVh9vDBlI9Pqr0HsPknjoiTycMDe8VLNTs4PNTgHUkHTDU8n2Ffkjo25Fu6SzKNLSWRRAwdbbN1tT3cx1EciUvTTRuCuXVHkZaI0+UI0aOiive4vWac/D37wdY/zIzjdiFCcwpzb0/bCmTw35NCJXEo89S/yXv0MNG0zJfd/HKB9I9OrLGDSoD/v3H8vNpn16QSQiwQ8hskhrTeLXwQ226E3XtPl5sRuvIfnYs8Tvf5TI1ZdlgpNdiVdRCRGrUyV6qrQvRCwJfoi8kneuol26YhZFc9+Ys11T3d1kGp7mO/MjlQYpE18Kk799NySSmNLvI+sagh9OyCcRuZJ8YRn13/sVqrQvJff/INPwL9eUUqgB/dDVR9GeTHwRIhu8N1fjvbMe66JZmONHtfl5qk8vop//CNTUkfjvP+XugDmiE0n8DVsw7NGoSMfvoyulUOUDpexF5JUEP0S7dMUsitjN1zV9Pcs11d1NJvMjz8EPNXhAsL8EPwpSQ7NTCR5mmzqtHFXaF2/NxrCPInLAfXN1kCkZi1LyP3dgjh6e1/3VgH7g++gjOcouEaKHSd8QjHbg/WT0uoWoEUNJPPI0/tZd2T5aTvlV2yDpYnai2WmaMbgMfaAa7Xbt6Tei65Dgh2iXrphFYV0+B/r1AUMFF4qLclJT3d3o6iNgGKi+vfO6b7pTutwJKEx+gYy57Y6UUphTbfTOvfhSntCteOs3UfuF74GvKfnltzNZPvnUMPFFmp4K0Vne6g14y97CnDUN66wz2v18FY1Q9JUbwPWo//mDWT9fLqX7UnWm2WmaGjIwCMoeqO70WkK0hQQ/RLt0xSwKf10VHDlG5H3ziE4dD66LddGssI9V8HT1UVT/Pnnv65AefSZlL4XJS2V+SNlLbhhTxgPIyNtuxN++m9qbvwPHayn+0VexLjgnlHMY6YkvByWwJkRnpbM+Yjd3/P2vdemFGFMn4P79Vdx31mfraDnnrUtPeslO5gdI01ORPxL8EO0SWTiXyDWXnXDNev9FBZ1FkXxxOQDWRbMpvvAcSLp4b68L+VSFTx86kvdmp0CmBl7LC2FB8iu3QnGRNKPNEXOqDUjfj+7CP3iY2pu+gz5QTexbnyXyvnmhnUWVpTI/qiXzQ4jO8DZuxX3xdcwzbcyZZ3V4HaUURV+/EYD4T/8XrXW2jphTfkUVmAbGhFGdXis98UXe84l8keCHaL9EEoChf/lPUAq9a2/IB2qZu3g5RCysC8+h+IKzg2tvvBvyqQqbdj300eN57/cBoAYFPT8k86PwaNfD37wDY9zpMuklR9LlEL5kfnR5+ngttZ/9Lv62XURvvo7YJ64M90AlxRCLysQXITop8UDQ5D9683UopTq1lnXeFKyLZuGtWhu8Xy1w2vPwnE0Y40aiYtFOr9eQ+SHv+UR+yLtX0W7uijWofn0ovvAczLMn4a2qwD9YmHeS/J178Z1NWLOmoXqVUDT7LDAMPAl+tEgfOQZahxP8iEaCqQTyQlhw/G27giZnBdzjp6szBvRDnVaOt2ZDl7kLKE6lE0lqv/QD/IpKIldfSuzW68M+UmriS3/0kWPSXFCIDvK37ya5aCnG+JFY82ZkZc3YVz8FpkH8Z78p+K9Nf8tOqItjTux8vw+QzA+RfxL8EO3i79qH3rkX89zJKMPAWjAbtMZdUpjR6uTidMlL0OPD7NsbY9JYvHc3oGvrwzxaQdPVqTG3IZS9AKjBZfh7D8oPfwVGmp3mhzllfDCSdGdhZ9WJpmnPo+6bd+EtfxtrwWyKvvvFTt8dzhY1oC/4OjPKXAjRPvH/+wt4PrGbrs1aBqQ5ZgSRD1+Kv3kHyb/8PStr5oqfaXba+X4fIJkfIv8k+CHaxV2xBgDzvCkARC6eHVx/4fXQztSSdAqhNX9m5po140xwXby3K8I6VsHT1eGMuU0zysugrh6O14ayv2iajLnNj4a+H1L60tVoran/0a9xn30F89zJFP/0GyjLDPtYGZmmpzLxRYh28/cfIvnX51AjhmBdNiera8e+8DEoLiL+qz+gawr3vY9XETQ7NbIw6QVSpc5KoaXUWeSJBD9Eu3grg+CHNX0qAMaIoRj2aNzX30IX2A+q+sgxvBWrMaZOyESWAczpZwLg/kNKX5qTrglXpWFlfgRpkHInoLB4qcwPKXvJrXTfDwl+dD2J//kTyT8+hTFhFCX3/huqKBb2kU6QGXcrE1+EaLfEg49D0iX2mWuyHtQ0Bg0g9ukPow9WE//NX7O6djZ566pAKUx7TFbWUxELVdYff4+UvYj8kOCHaBdvxRroVYzR6JuetWA2JF3cV1aEeLJTuS+vAM8nctHsE65b500GU/p+tCSd+WGUhpj5Aeh98mJYSPzKbVBSjBo2OOyjdGvmpHFBb6I1EvzoShKPLCJ+z+9Rp5VTct/3UX17h32kU6iyVOZHtQQ/hGgPffgYiYefRg0aQOSqi3OyR/SGD6HKSkn85q/4+w/lZI/O0L6PV1GJMXo4qldx1tZV5WXofVLqLPJDgh+izfwD1fibd2CePemEiHfk4vMBSL5YWKUvycXBedL9PtJUrxLMyeODhoI1dWEcreBlyl5CCn6owemJL4X34t9T6aSLv2UnxtgRBdO/oLtSvYoxxozAW1uJ9gq7+Z0IJJ9/jfrv/xeqtC8l93//hGzDQqKKi6C4CF/KXoRol8QfnoS6eqKf+hAqGsnJHqpXMbEvfgzq6on/6g852aMz9I69cLwWI0vNTtOM8oEQTwTN9oXIMSuXi9u2PRy4BxgM+MDHHMfZ1ujjLwK9ABf4u+M438/leUTneCvXAsFYrsYMezRq+BDcpW+gE8mcvSi0h04kcV9ZiRoxtMn+BOaMM/HedfBWrcV6z3khnLCwZRqehhb8SGV+SNlLwfC37gLXxZRmp3lhTp2AX7kVf9N2zPGjwj6OaIH7xrvU3XYnFMUo+fUdmKOGh32kFqmyfuid+9C19aiSojY9Z8mS55u8XlISpbY2ccK1+fMv6fQZhciVpj6Xm/o8hobPZV1TS/yhJ1D9+hC95vKcni/y4UtJPPQEyb/8nej1VxZUmWm630e2mp2mqfJG7/lCarQveo5cZ37UAHc6jnMB8HvgtpM+PgB4j+M4F0rgo/B5JzU7TVNKEVkwC2rqcJe/HcbRTuEufxtq64hcNKvJu9TWzLOCx0npS5P80Buepnp+5LjsJbloKcevuoWqIfM4ftUtJBctzel+XZlMesmvTN+Pd6X0pZB566qo/eIdoKHkl9/GnDIh7CO1SpX2A63xN21r/cFCCBKPPgtHjxP9xJVZLfdoirLMYPSt7xP/2YM53au9GoIfOcj8AOn7IfIip8EPx3GqHcdJz0DdBfQ/6SGW4zjJXJ5BZI+7YjXEok2+ubMWBKUvboGUvmSmvCyY1eTHzWkTwTIl+NGM0Bueluc+8yO5aCl1t92Jv2ELeB7+hi3U3XanBECa4VcFPyiZMuklLzLBD+n7UbD8bbupvfm7UFNH8Z1fwzr/7LCP1CbpiS9epQQ/hGiNjidIPPhXKCkm+tH352VPa/5MzHMn4y5ZHrz3LhDeutSY2yyXvahU8EMmvoh8yGnZy0k+DPztpGtR27ZfSv3/9x3HebGlBUpLS7Da0V150KA+7Tpg2Ovmcu3OrutVH+Xohi0UXXA2g08bcMq6+r0z2DKwP/5L/2DggBKU2fku2B09s/Z9ti59A2NAP4ZcMgNlnfhpPmhQHxjUh+TZE6lfWcGAmMLMQmO67vR5UX+sBr9XMYOHd6xuvbPn1QN7UxOLYh46fMpa2fq72P5/jzV53fvNnxn2ySuysgd0n8+LPdt3EQcGzZyE1cF9u8vfRT7W1edPZXMsilpX2e4zdLe/i0JbF6BUJ9n5ue+iD1Yz8Edfpt/12fme0dkzl5REW73uDhvIMSC2cxcD27hfc+s29bH2/Bnas2621s7Vuk19TP4ucr9uttZubt0jv32CY/sP0f+fP0rZ+GFt3qej50ur/49/Zudln8P7+YMMefbXTWYx5/N7p9aa4+ursEYNY/DYoVldu9YewW6g+NhRBhTY+wt535L7dfMtL8EP27YXAsOBPze+7jiOnfr4aOBl27Ztx3GanZdaXd32UaqDBvVh//7sN87J1bq5XDsb6yaXvBGkyZ55Rmatk9c1580k+ee/s+eFN7HOmRzamb13Hby9B4lcdTEHqk9saNp4Xf+cyfDmGvY+9w8ic2eEdt6w1m5p3eT+Q9C/b4f2zdp5Bw8gsXPfCWtl8+8i4Wxp9nq29uhOnxd1a6ugdwmHIsWoMD8v8rh22Osa9mgSFZXs23EQFWv+h4GOrN1esm6QLRa/75EgC8qyIJ4g+rl/InHle7OyVzbO3FTfgpP7GeiSEgBq3q1Et3G/ptZtam2gXX+G9qybjbVztW5za8vfRe7Xzcbaza27b/dhjv/89xCN4F69ML/viU4/Heuy9xB/9hV2P7SIyOVzsrNuK5pb19+9H//gEYxzp3Z43+bW9mLB96Tjm3fhFdD7C3nf0rF1Cz1IkvNpL7ZtjwF+CnzCcZwmZxg5jrOZoCzmtFyfR3RMut/Hyc1OG8uUvrwQbulLMl3yclHTJS9p1oyg74f3Dyl9aUxrja4+Glq/jzRjcBn64GG0m5tpF+q0IU3vW0DNxQqFTiTxt+7EHHu6THrJI3OqDa6Ht35T2Efp8U4sk/MhHvyg1BW/X6iiGPQqwdu4NeyjCFHQ3GdeRu/YQ+TD78UYNKD1J2RZ0ZdvAMui/ucPohPhdgnIVb8PIDMdy5cm9yIPchr8sG27N/AI8GnHcXanrt1h2/YHbNsusm27b+raIIKJMFKAWqDcFavBMjHPOqPZx1izp0GvYpIvLgt1Vre7eDnEoljnn9Pi48xpZ4BlSd+Pk9XWQSKJEdKk6ojPegAAIABJREFUlzRVPhB8H32gOutra9eDZkaIxm66Nuv7dXXBpBevyclJIneMqeOBIJtNhCt+3yNNXk880HT5XKFTZf3Qe/ajj7c9o1aIsHgbt5B4+Gmq7/4tiYefxtu4Jed7aq2J3/8omAaxT1+d8/2aYpw+lOg/LURv30PikUWhnCHNq0j1+8jypBcIRvzSpxd6rzQ8FbmX68yPLwKjgbtt2341Ndq2HOiT+vWibdvLgKeALzqOE8/xeUQH6Jo6/IpKzCkTUMXNj8VT0QjWnOno7XuCu2Mh8Lftxq/cijV7Wqsj/FRxEeZZNv66Kpkt3kjYzU7TGu4EZP/FMPHQE+idezHPnYxhj4ZUNkPsq58isnBu1vfr6mTSSzjMqTYAvjQ9DV264W9brxe6dHDbq5TsD1HYvI1bcJ97DX3wcHBD5OBh3Odey3kAxN+8A79yK5Er5mOcVp7TvVoS/fxHoHcJif/+E/ro8dDO4a8LMj+MLDc7TTMGl0nmh8iLnPb8cBznx8CPW3jI9FzuL7LDe3sdeP4pI26bElkwG/eZl3FfWIZpj87D6U6UXByU3LRW8pJmzjgLb+Va3JVribTxOd2drj4KpMYhhigz8SXL3b/9HXuI/+ohVGlfin/5bYzSfhT/YxV7P/VtCYI1Iz0VwpTgR14ZI4dB7xK81RL8CJsx9vQmg/pdsewFGsaY+5VbYdrEkE8jRPO8lWubvr6qAnP8qJzsqbXGW7UWlCJ64zU52aOtjNJ+xG66lvh/Pkj8gcco+uqnQjmHV1GFKi/DKDt5cGd2qCEDoWobuq6+xRutouuzbXs4cA9B1YcPfAxIAn8AegFPOY7z/Vztn/OeH6LrS4/ZMs9tPfhhzZkOEYtkSH0/3MXLQSmseTPb9Hhr5pkAeFL6kuFXF0rmR1Bfq/cdytqaWmvqvvcrqItT9M3PZu5+llw8C3qXkHx6Kdr3s7Zfd+FXpTM/uuYPel2VMgzMKRPwt+wM9Y6fgNjN1zV9vYuWyanUuFtf+n6IAqdT70lOuX6o6etZ2XPHXvTeg1gXz8YsgABn9BNXooYMJPHQE/i79+d9f3//IfS+gzkpeUmTvh+FKbloKcevuoWqIfM4ftUtJBctzcayNcCdjuNcAPweuA24gyAgMgu4xLbtzk3OaIEEP0SrvBVrwDCwzpnU6mNV7xKsWdPwnU34O/bk4XQN/OojwZ2As87AGFjapueYZ50RdPGWpqcZmcyPkBueqsHB3Pdslr0k/7YE77VVmBeei3XFvMx1oyhG5JIL0Hv2462qyNp+3YVfuQ369EIN7tjoY9Fx5pRU3481G0M+Sc8WWTi3Id3bMjHs0RTfdXuXLZNLf3+XshdR6JrNQtUa960KtOtmfU93VdDkv1CCm6ooRuyfPwHxBPF7Hsr7/t66oN9HrkpeIJX5AdL3o4Cc2Ojbw9+whbrb7ux0AMRxnGrHcZanfrsL6A8sABalhqMsSv0+J1SYjSnby3U9bVlm2McQQgghhCg4Tz31VJsfe8UVV3SpdXO5dldbN5drd7V1c7l2e9YVXZt8XjTIwt9Fm8cC2rb9IPA0cI/jOENS1z4DjHEc51/bfJB2yGnPj2yrrm57V/JCmHNcKGt3Zl13xWpqr7+d6PVXUfTNm9u0rn+gmuNzP455ziR6PfTTvJ259ks/wH1hGb2e+jXmmBFtXjf+X38k/qvfU/zLbxO5+Py8nTfstZtbt/5nvyHxwGOU/OEurLNbz/Zp67rtpRNJjk27EnPmWfT6zY86vXbdN+8i+eRiYt+4kdgNHzrlzPv2HOb4/Osh6dJ76e9R0Uin/wzd4fPC27CZmqu+QOSayyj+3peytm42FeL3zmyt6+85wPGLrsdaMJuSe76T1bXbo6evqw8f49j512FeeC6jHv95wZ65tjZxyrWSkmiT17d++Ku4L79J72UPY/Rvucyxqec3t3Z7/gztWTcba+dq3ebWlr+Lzq2rtSb51+fRe/ZDvz5wrAY1oB/mOZMwRgzFe3sd3rsOVZ+6EzV8CLEvfIzIFfNQZvM3Slv7u0guWoq/eQeRqy7Oytd5Nr8Xua+upPbm70BJMcQTGGNHELv5uqxmoDV13tpbf4D7/DJ6L/4dRipDI1trpyWXvkHd5/+d2FduaHfGTb6/dxba50W2103/meP//Ufwm0iSMAxin/8I0PTfxaBBfdq0j23bC4HhwJ+BXzf6kAKi7Tp0O0jZi2iRtyJI/WtLs9M0Y2Ap5tmT8FZV4B88nKujnUDXx3FfW4kx6rRmAx/NMWek+n5I6QvQeNpLyGUv0QiqtG9WGp66y1aRfHIxxuRxRD9+ZdP7mSaRhXPRR47hLlvV6T27Cz/V7FT6fYRDlZehBg3Ak4kvofKcTQCYZ4wJ+STZY4wPGhj7UvoiCpS/ZSd6z36MMSOIffwDlH7tk0SvW4g5fhSqKIY1axrRj3+A6CeuRO89QP237qbmyltIPvcqHcls9w9W42/egRo6CDVscA7+RJ3jp5uy19ZltQyhNV5FFWpAv0wj+lyQnh+Fp7mfA7JRFm/b9hjgp8AnUqUux23bTne6HQjkrHeCBD9Ei9w3U81Oz2lf3xlrweygHnPJ8tYfnAXu8rehLt7mKS+NmWfaEIvivvFODk7W9aSbixkhNzyFoO9HZ18IdV190OTUNCj+3q2oFkrnIu+bB0Dy6Zc6tWd3ku4JYI6VSS9hUEphTp2A3nswJ2OfRdt4zmagmwU/xqWDH11zXK/o3rTv4y1/B5TCnHlWs49TJcUUfeuz9H7mASJXX4q/dSd1X/4hNdfcivvKinYFQbxVwWQZ85zJKNXmzP28Sdz/aJPX481czwZ9+Bh6516MSeNy+nciPT8Kj3lu0z/7mW3oAdkS27Z7A48An3YcZ3fq8ivA5bZtK2AhkLOIngQ/RLN00sV7ex3G2NMx2hnli1w8GwA3T1Nf3MVBkKUjwQ8VjWCePQl/41b8HHYP7yp09VEwDejbO+yjYJSXQW0d+njbS95OFr/3D+jte4h+8oOYk1pu1mVMGY8xchju4uXomroO79mdZDI/xkvwIyzmlAkAMvI2RP76IPPDCGGEe66Yqa9pb+OWcA8iRBN8ZzP60GGMM8a06T2oMWwwxXfcSq+//RrrfXPxKyqp/ex3qb3+G7ipLOaW6MPH8DduQw0sDcaMFyC/qulAZXPXs8FbHzQ7NXPY7BRA9e8L0Qh+FrJ9RXaY40dhTBgV/MZQqIGlWO+9IBsjpr8IjAbutm37Vdu2XwS+AXwJWA4sdhxnRWc3aU6LwQ/btmOtfPyi7B5HFBKvohLq4pjTp7b7ucaIoRj2aNzX3+rUD65toX0fd8kbQR3oWWd0aA1rhoy8TdOHjqD69yuIux7pFMuO3vH2KqpI/PZx1IigFrjV/ZTCet88qItnAmo9nV+5Ffr2RrVxgpLIPvPMVPBDJr6Exlu/CYpiGKNOC/soWWOMHg5KSeaHKDja9YLMY9PEaud7UHPUaZT89HZ6PX4v1vxZeCvXUnv9N6i5+Tt4a5v/Huq+XQFaY54zqSDe/zTFaGbsbnPXs8GrSAU/cjjmFoL3X6q8DL1HMj8Kiu8D0O/GqzMlZ53lOM6PHccZ6DjOhalfCxzH2ek4znzHcWY6jvPvLT3ftm27mesfaur6yVprePoKMMO27R828/F5QMc6RIqCl+73YbWj30dj1oLZJJzNuK+sIHL5nGwe7QTeuw76YDWRD723xSZXLTFnBsEP9813iVz2nmwer8vxDx8NMi4KQLoGVO89CO18cdeuR913fwGeT/G/fRFVXNT6kwhKXxL/9UeST79E5P3z233m7kTHE/jbdmNOm1iwbwZ7AnNyatztaifkk/RMOpHEr9yGMXFMh19jCpEqLsIYMRR/41a01vI1LgqGt2YDHKsJXnv69OrQGqY9mpJ7v4v7znriP/8t3qsrqXl1Jcn3n4s546wTskn847X46zah+vXJaSChs2I3X0fdbXeect2aOyNne3rrKgFazZzNBmNwGd5b69Cu12KJssgff+9BKC7C6NcbmmlwnA+2bZc0+u09tm1fTdAbBOAY8H7gRuCvra3V1rKXDwPlwD7gfcBQQPKSujlvZarZaTM1X61JT05Jvpjb0pfOlLykmVMmQHGsxzc91UkXjh7PSjOjbMhkfnQgDTLx0BP4FZVErlyAdf45bX6eOXo4xuRxuK+t7PFlUP7mHeD70uw0ZKpfH4yRw/DWVqJTd2FE/vibd4Drdqt+H2nGuNPRh4+i89ScXIjW6OO1eCvXQjTS4fefjVlnnUGv3/yIkv/7IeZZZ+BXbSf58NMkX1yG+856Eg8/zZH/eRR8HzW8HGUUbkeAyMK5FN91e1B+Z5mo04eCYZB89Bn8nXtzsqdfUQV9eqGGD8nJ+o2p8oHg++gD1TnfS7RO19bBsRqM8gFhHwVgL0ET1PREghuBJ4E3gJ8AH2nrQs1+hdu2/WGgNJVCcgh4B3ieIACyGnihIycXXYP2PNyVa1EjhmKUd2yslWGPRg0fgrv0DXQimeUTNnAXL4eiGNbsaR1eQ0UszHMm42/ajr//UBZP17Xow0eBVO1lAVCDUpkf7Qx++Dv2EP/VQ6jSvsS+cWO7941cMR88H/fvr7T7ud1JegqEOU76fYTNmDIBjh7H37Yr7KP0OF6q34fZjfp9pGUmvmyUiS+iMMR/8xeoj2OePQlV1GL1fbtYs6ZR8se7sRbORZX1x1+/Ge/VlUHgL9UU1V9bWfA9cCIL59L78XsZu/sl+jz7vxR9+/Pow0ep/fJ/oOPZvTOva2rxt+zEnDQ2L5lh6Z83pLl3YUjfeFSDOz7eOItWACuBvzW6dg9BTKJd0fuWwpvnASXAdKD986JEl+Zv3ArHajpc8gJB/V5kwWyoqQumseSAt2UH/qbtWOef3eayhuZk+n6kJtz0ROlJL4WS+WF0IPNDax1Md6mLU/TNz2J0YGRv5PI5oFSPn/riyZjbgpHu++Gvlr4f+ZZpdtoNMz/SgU1Pxt2KAuAfqCbx4ONQUtzhHm4tUUphjh5O5NrLoZlyGm9VRdb3zaXIdQuJfPAS/LWV1N9xb4dG/DbHW7856IMyMbf9PtLS2b4y8aUw6L3p4EdBZH5AEI/QTfy+XZ/0zQY/HMf5FrAz9d8BwFRgPjAImEzQ70N0U5kRt50IfkBq5C3g5qj0xX2x8yUvaWYq+OH24KanOlXmofoXRvCj4YWw7cGP5N+W4L22CvPCc7GumNehfY3BZZgzzsRbVZGzVNKuIJ35YUjmR+gaJr5I3498y4y57c6ZHxL8EAUg/j8PQ1091vQpqEhrbQk7TikFNU0349ddrNxVKUXRd27BmDyO5OPPk3xkUdbW9iry1+8DJPOj0KRvPKb77xUI1ei/PyHoPXoT8B6gTTXurRW2pTd4iiClZCRB6cshYHg7Dyu6kHS/j/Z22T6ZefZE1IB+wehQz8vG0U7gLlkOhoE1b2an1zInj4eS4h498UVXB2Uv7R1tnCvtHX3mVx8hfud9UBwLmpx2Ik0zkgqcJBflbNR4wfMrt6H690WV9Q/7KD2eOXEsmIZMfMkzrTX++k2oEUNQvUpaf0IXY4waDqYhwQ8ROn/7bpKPPhOUW+ch00A1kxVaKJmv7aGKYpT8/F9RpX2p/+Gvcd9el5V1/XXBpBcjx5Ne0jpyw0vkhtY6+Hfo17vTmfU5ooHbgdeBBwiGtKxq8RkpzYZVbdu+imDeLo7j3NbMY37V3pOKwqe1xluxBjVkIOq08k6tpUwT66JZJP/8d7y312NloXlVmn/wMN5b6zDPnpSVH9aVZWKdOxn3lRX4+w4WWqQzLzJlL6UF0vNDKdTgsja/EMZ/8gC6+iixb9yI0cnP3cglF1B/x70kn1pC7KZrO7VWV6Tr4/jbd2OeO1mmQBQAVRTDGD8Kb10VOunm9K6oaKD3HUQfPoo1vXNZkCdbsuT5U66VlESpbaKb/vz5l2R178ZUNIIx8jQ8mfgiQha/5/fguhTdej3KzP1UCfPcybjPvXbq9XMmdWi9pr6moemv61x8TRunlVN81zepvenb1H35h/R67BcYgzpXruBVVAaTPkYOy9IpWyaZH22X88+3I8chnsA4fWhHjpdLuSt7Ac4FHrZt+1Xbthc39Qvo2HcIUdD8zTvQh45gnjslK2+ErAXB1Jdsl764L70BWmel5CUtXfrSU6e+pDM/mrsjEgZjcBn6QDXabTlzyF22iuQTL2JMHkf041d2el/VtzfW3Bn4G7fibdjc6fW6Gn/TDtBaSl4KiDnVhnhCmlPmUUOz0+7X7yPNGDcSjtfK3VYRGm/9JpJPv4QxcSzWZe/Jy57m+FFY770ANbAUDAM1sBTrvRdgjh+Vl/1zwZo9jdhXbkDvO0jdV34UTPDrIF0fx6/ahpnHEd9qYCkoJd+LCkBDs9OCuRE8haAFx4LU7xXBxJfJQLvSk1vq+fEdYDRBJ9UxBDN0bwI+cdIv0c14K1IlL53s95FmzZ4GvYpJvrgsq42YsjHi9mTWzFTfjzd7ZvDDL7CGp5BKg/R99MHmR5/puvqgyalpUPy9W7M2Hz7yvnkAJJ96KSvrdSVeZtKLNDstFOmmp9L3I3/89UHgszs2O00zMxNftoR7ENFjxf/zQdCaoq/ckNdRs+b4UUSvW0jp1z5J9LqFXTrwkRb99IexLr0Qb9Va4j99oMPr+Bu2gOfnrdkpBJMX1cBSyfwoAOkpi+nBAwVgXurXRwkCH78DPg8sBL4PPNrWhVr7DnOx4ziPABOBlwHlOM7Oxr/af3ZR6NLBj842O01T0QjWnOno7XuCb6ZZoOvqcV9/C2PMCMxRp2VlTQBj4ljoXYLbUzM/DhVW2Qs0NFpq6U5A/N4/oLfvIfrJD2a1MZc1d3oQuFu0FO37WVu3K5Bmp4Un0/R0zYaQT9JzeOuDmvfu2Ow0zchMfNkW8klET+S+uRr3lRWYM8/EvKBN/QpFC5RSFP/gKxhjTyfx+ydJPLm4Q+ukm50aeWp2mqbKg1LnbN4sFe3n7zsIhkINLIxJL47jrHUcZy3QG7jdcZz9juOsTP3aDCwH/rcta7UW/LgrtWEd8BZwum3bF9m2Pc+27cK5NSyyRmuNu2I1qrQvxpgRWVs3cnFq6ssLy7KynrvsLaiPZzXrA1I9Ss6bgt6+G3/3/qyu3RVkyl76F07wQ7Uy7tarqCLx28dRI4YQ+8LHsrt3UYzIJRegd+3Deys7DcS6Cr8qPeZWgh+Fwhh7OhTH8FZL8CNffGcz9O2NGjY47KPkjCGZHyIkWmviP/sNAEVf+ZT0nMkS1auY4nu+Db1LqP+3X+JVVLV7DS/V7NTMU7PTNKN8ICSS6MNH87qvaKA9H73/EGpA/0LsL7YU+LZt23fYtj0HwLbtkcCTQJt+cGvrtBcI0kmuBq4hKH9pc3qJ6Dr0zr3oPQcwz8tOv4806z3TIWKRfCE7fT8yJS+pUbrZ1JNH3urqI9C7BBWNhH2UDKOF7t/a9aj77i/A84PpLjnoSJ2Z+vL0S1lfu5B5lVtRA/oVzOQfETRlNieOw6/chq6tD/s43Z6urcffugvTHt2tfygzTh8GEQuvSjI/RH65i5fjvbMe65LzMc+0wz5Ot2KOGk7xj2+DeILaW3+APnysXc/3KiohGsnqjdC2kIkv4dOHDoPrZf4tCsx24HvATuDfbNt+B3gB+JLjOE+2ZYH2FNbtdhznFsdxPg/cAAxp52FFF+BmSl46N+L2ZKp3CdasafjOJvwdezq1lvY83KVvoAaWYk6dkKUTNrBmnAWA9493sr52odPVRwuq2SmAGtR85kfioSfwKyqJXLkA6/zcpMuaM85ClZXiPvtKp5qHdSW6rh69Y69kfRQgc+oE8P1MSrLIHW/jFtC6W5e8QBBUM8aMwK/c2uPK+0R4tOcR//lvwTCI3frJsI/TLUUumkX08x9F79xL7dfvRHstN45P04kk/oYtGPbovN/5T5c6+xL8CE068FRIUy9t2/4v27a/lfrtu8CbBP1IDxLEMwa1da3Wgh8nj5NJ84APt3UT0XVku9lpY9bFwdSXZCenvnhvr0cfOoI1f2ZOGmMZZ4yGvr1x31yd9bULmdYaXX204O70ZzI/Tgp++Dv2EP/VQ6jSvsS+cWPO9leWSWThHPTho0G5VQ/gb9qemvQizU4LTTrgK6UvueenJr1052anaca406Eujt61L+yjiB4i+cSL+FXbiHzwYsw8Zxf0JLEvfBRrznS811YF44TbwK/aBkkXc2J++30AqCHBuFstTU9DU4CTXgD+QFCRsgM4RFD+cr/jOBcBFwLfsG37/W1ZqMmfHG3bHmXb9pPARNu2N9i2vREY3+j/1wPP2LYt7766GW/FGujTC2PCqKyvbc2fCUp1uu9HLqa8NKYMA2v6VPTOvfg79+Zkj4J0rAZct6CanULDN9/GdwG01tTdcS/UxSn65mcxcpyt0jD1ZUlO9ykU6caHpmR+FJxM8EOanuZcZsxtDwh+mONGAQ1TnoTIJR1PEP/VHyAWJfaFj4d9nG5NGQbFd96GGjGUxH2PkGzDe/B0j5B89/sAyfwoBHrfQYhYBTX5ERhOEADZC1wBPAgcsW272HGc3cA/E2SCtKq52+bHgf8E1jmOM8FxnPFAZfr/G/+3s38SUTj8fQfxt+3COmdSTmZ6GwNLMc+ehLeqAv/g4Q6tobXGXfw6FBdhzZqW5RM2MGcEZT9uDyp9yTQ7LbSyl1gU1b/vCZkf7lMv4b26EvPCc7FSPTlyyZg6ATViKO7i13tEr4WGSS+S+VFo1PAhqP59JfMjD3xnM1hm0Gi2m0t/rfsbJfghci/xp6fQe/YT/dj7MVJ3+kXuqH59KPnlt6E4Rt237sbbtL3Fx3vrgrJKyfzoeXRNHfrQEdSgAXkdO90Gm4H5wDCC4MdPU79etW37MuC7QG1bFmryT+U4zgHHcU6+xSkzh7o5b2V2R9w2xbp4NmiNu2R5h57vb9qOv3UX1oXnoGLRLJ+uQabvRw9qeupXF96Y2zRVXpaZ++5XH6H+x7+G4ljQ5DQPjQiVUkHj07p4hz93uxIZc1u4lFIYU8ajd+zJfM2K7NO+j+dsxhg9PKevNYXCHD8KaPjaFyJX9LEaEvc9Cn16Ebvx2rCP02OY9miK77gVauqo+9IP0Meb/znRr6gC08hJFnhrjMFB8KO5CX8it7x1lUHZc2GVvADcDXwGOBeYA3wWGABcBHwH2OE4zhttWag9XWyG2rb9s0a/P+o4zr+34/miwLlv5qbZaWORBbOJ/+QB3BdeJ3r1Ze1+fkPJS/anvDRmjB+J6t8X943VaK27daf/tELN/IAgDdJ3NuMfryX+kwfQ1UeJfeNGjNPK83aGyPvmkfjvP5F8+qVMGUx35VVuQ5WVYhTQyGPRwJw6Ae/VlXirN2DMmR72cbolf9tuqKvvESUvAGp4ORTF8CTzQ+RY/Dd/QR8+SuzLn0T17xP2cQrekiXPN3m9pCRKbW3ilOvz51/S7FqR983DW7OBxG//H3X/8jOKf/4vp9zd156Ht34TxriRoQR+VUkR9O2N3iOZH2Hw3g2ySgus3wfAAuAM4BGC8pY/AtcTTH7pC4y0bXuQ4zitjrttz6jbG4AXG/16pd3HFgXNW7EGimM5rfEzRgzFsEfjvv5Wi1Hn5riLl4NpYM3N7Rt+ZRiYM6ai9+xHb+/cdJquQh8q7MwPgGMPP0PyiRcxJo8j+vEr83oGc8wIjEnjcF9d2a3vuPvHa9E790rJSwFL9/3w12wM+STdl+9sBsCwe0jwwzAwxo7A37S9zRMhhGgv/0A1id8+jhpYmvfXcBGIfe0zmDPOxH1hGYkHHjvl48nKbVAfD6XkJc0YXCaZHyHxVjtAw7CBAvIlYAawB/gP4CaC5qfLgN8DdwGfb8tCrWV+/Gv6fxzHebojJxVdg199BL9yK+bsaTkfa2UtmE3C2Yz7yptELp/b5uf5+w/hvetgnjclL3ekreln4j73Gu4b7xA9fWjO9wubPpwKfhRWgyMA/CNBD6MD3/o5AJFLLkRZ2e9L05rI++YRr6jE/furRP/pfXnfPx8SqTu/0uy0cJlT0hNfnJBP0n1561MN/87o3mNuGzPHjcRfW4m/fTfmqOFhH0d0Q/H/+RPUxYl9/cbgDr/IO2WZFN/9TWqu/mfiv/gd5uTxWBeck/l4fHUQVA+j2WmaGjIQKreia+vl8yTPvNUbYGgR9OkV9lFO4DjOXQC2bb/sOM4G27ZXARMcx3krdd0iGIHbqhYzPxzHebKzhxVdg7dyLZCbEbcni2RG3ravd4L70hugdc6mvJzMnHFmsO8/ekbfD32oMMtekouW4j1/Ynfy+M8fJLload7PElk4B5Qi+fRLed87XxLOFkCanRYyY2ApauggvDUb0VraceVCT8v8gIYeP35q2pMQ2eRv203y0WcwTh9G5MOXhn2cHs0o60/JL74Nlknt136Mv6MhwzmeKnswJoWb+QHg75PSl3zyD1Sjd+3DKB9QsOX+juNsSP23Jh34SP3edRynsi1rFFQbVxGehmanuev3kWbYo1HDh+AufQOdSLb5ee7i1wGI5Cn4YYw7HVXWH+/N1T3iBwydKuXI9djY9orf90jT1+9/NM8nAaN8IOaMqXgr13bbMcjJ9A99PWDCRVdmTrXRBw+jd+0L+yjdkrd+E2rQAIyy/mEfJW8ywY+NW8I9iOiW6u95CFyP2K2fyHmGsWideaZN0XdugaPHqf3SD9B1wSS7+LsbQCnMEAO/6VLnsPp+eBu3kHj4aarv/i2Jh5/G6yHfE7016X4f3XsCkwQ/BADuijUQsTK15LmklCKyYDbU1OEuf7tNz9E1dbivv40xfiTGiPyUoCilMKdPRe87iL91Z172DFM6+FFoZS9+VdN3IZu7nmvpZqfJZ14OZf9cS6wPgh9S9lLYzKniCzwuAAAgAElEQVTjAWTkbQ74h4+i9xzoMc1O08zxwde8J5kfIsv8/Ydwn34JY9I4rEvfE/ZxREr06suIXHMZ/vpN1H/vV2jfJ/HuhmDKVa/i0M5llIc38cXbuAX3udfQBw+D76MPHsZ97rUeEQBJv59QgweEfJLckuCHQB+rwV+3CXOqjSqK5WVPK1X64r74epse7y5bBYlk3kpe0qxU6YvXA0pf/ENHwLIgxBe8pjSXgRBWZkLkkgvAsrpt6UvC2YIaWCpd+AucOdUGGu7UiOzx128CgizFnkQNHQS9ivFl4ovIMu8f7wBQ9JUbTpkuIsJV9K+fx5g6geSTizl+4Ufxj9Xg7z8USmlxWpiZH+k2AKdcX1WR55PkXzr4UYBjbrNKvgMJ3LcqwPcxp+e+30eaOe0M1IB+uIuXt6mzfDpIEsnxiNuTZfp+vNH9gx/68FHUgH4FV+cXu/m6pq/fdG2eTxJQ/fpgzTkP39nc7e4E6Jo63O17MunvonCZk8eBUpL5kQNeqvTLnNizMj+UUkHT0y072lWSKkRL/J178bfuwpx5Fub5Z4d9HHESFY0QufJiIHgfCMCxGupuuzO0AEiYmR+6mWl+6YmI3ZXWGu9dBzViCKq4ezeZleCHCEbcAlYe+n2kKdPEumgW+uBhvLfXt/hY7Xq4S99EDS7DmJzf7tPG6OGogaV4b3T/vh/60JGCa3YKEFk4l+K7bg/uwlomhj2a4rtuJ7Kw7ZOCsn6mK+YDkHzqpdDOkAvpUiJTmp0WPNWrBGPMCLy1lTKaNMsaMj96VvADUo2OXQ9/666wjyK6Aa017utBeXPRVz9VcDdXRCD56KImr4fRWw0aZX7szX/wo7n3wYVWEp5tettuOHo8k1XanUnwQwTNTk0Dc9oZed3XWpAqfXlhWYuP81atRR85hjV/Zt7TJZVSmDPPRB+sxt+0Pa9755NOJKGmDlWa+xHCHRFZOJfej9/L2N0v0fvxe0MNfABY82ZASTHJp1/qVkExrzJId5fMj67BnDoBauvwN+0I+yjdird+MxTFMEYOC/soedcw8UVKX0Tn+Zt3oPcewBg7Ii895UTHFFpvNdW/L0Qj+HvzX/Zinju5yevGmBF5Pkl+pUtoe8LXqQQ/ejhdV4+3eiPGpHGoXiV53duaPQ16FZNc/HqLP0C6i4ORuPma8nIya3qq70c3Ln0p1GanhUoVxYhccj561z68t9eFfZysSC5aSvzu/wMg/r+PhVrvK9om/SZF+n5kj04k8au2YYwfiTLNsI+Tdw1NTyX4ITpH+z7e8nfAUJgzp4V9HNGCQuutppRClZeFkvlhjh+FMWFU+iDQt3dQYrq2El1Tl/fz5Eu6hFaCH6Lb8951wHWxzs1fv480FY1gzZmO3r4Hf8OWJh+jtSa5eDn0KsaceVZ+D5iS6fvRjZuepmsZVf/CzPwoRJmpL92g8Wly0VLqbrsTXR3U++rte0Kt9xVtY05JBT+k70fW+Ju2g+v2uEkvaZL5IbLFdzajq49gnDEGo0CzSkWg0HqrQdD3Qx+oRifd/G/uBnv2++w1xD5xJeass6CmluRzr6I9P//nyQNvtRNUAUwcG/ZRck4Gbfdw7purATDPy3/wAyBy8WzcZ17GfWEZZhOd9f2NW9A79mBdeiEqGgnhhGCMHIYqL8N7812073fLTuXpH3oNyfxoM3PWNFRZf9xnXkbffjMq0nW/ncbve6Tp6/c/GnqJkWieYY+GiCWZH1mUaXbaQ4MfatAA6NtbJr6ITtGuh/vGarDMTPasKFzp1/n4/Y8GmW9jTyd207Whvv6r8jLQGn2gOphElSdaa/zd+6FPL4w+vaA2gXn2JPS+g/hV2/GWrYKLL23zekuWPN/k9ZKSKLW1iROuzZ9/SafO3lE66eJVVGGMH9Xtm52CZH70eN7KNaAU1jlN17jlmvWe6RCxSL7Q9MjbhpKX/E55aUwphTXjTHT10dDqH3MtU/Yid2faTFkmkcvmoKuP4r7+VtjH6ZRCq/cVbaOiEYwzxuCv3yzTObLEX18F9Lwxt2mZiS/bdqPjidafIEQT4m+tg+M1mFNtVO/8llSLjim03mqZiS957vuhDx+DujhGo4CLUgrrotmoAf3w3nVIPLk4r2fKNX/jVognekSzU5DgR4+mE0m8t9djTBiF6t8nlDOo3iVYs6bhO5vwd+w55ePJxcvBNLDmnBfC6RqYM4KSG6+blr746bKXApz2UsisK+YB4Hbx0pdCq/cVbWdOnQCum5lQIjrHW5/K/OihwQ8AY/xI8P1Qmnx7G7eQePhpqu/+LYmHn+5248S7O2/jFhJ/eoq6l94EpVD9wnlvKbq+sCa+6N37AU4IfkCqVP+yORCNUP/v9+Ctq8rruXLJW+0AYJ7Z/ft9gAQ/ejRv7cYg0hdCv4/GrIuDqS/Jk6a++HsP4K/ZiHne1NBfQK0ZwRhgt5s2PU3PdpeGp+1jnmmjRgwh+cLr6Lr6sI/TYeaF5zZ5Pcx6X9E20vcje7TW+Os3oUYMzXsD8EJiZvp+5Dfzy9u4Bfe519AHD4Pvow8exn3uNQmAdBGZf7/UzRS0xn3pH/LvJzrEGBxO5oe/Zx8AasippTZGaV+sBbOhPk7trf8RZIl0A5lmp1Mk+CG6OW/FGgCskPp9pFnzZ4JSuC+eWPriLvlH8PGQprw0poYPQQ0dhPfmarTf/ZodZRqeStlLuyilgsandfWZz9euRmudmWRkjDoNLBPDHk3xXbeHnvYqWicTX7JH7z2IPnIMc2LP7PeRZqQnvuT5h1Zv5dqmr6+qyOs5RMfIv5/IJjUkCH6EkvkRizZ7M9AcM4Lo5/4JvWMPtd/4Cdrz8nq+XPBWO1Ac6zHZvhL86MHSwY/mZlrnizGwFPPsSXirKvAPHs5cT4Y84rYxpRTWzLPQR441O5mmK0s3PJWyl/br6lNf3KVv4K/ZiHXphfRedH/B1PuKtjFGD4dexZL5kQWeE5QO9eSSF2g08SXPPX/SvadOuX6o6euisMi/n8gmY3BQ9pLPzA9dW4c+fAxjyMAWhxvEvvAxrPech/fqSuK/+n3ezpcLuqYOv2o75qRxKKtnjHeX4EcPpT0Pd9VajFGnYQwaEPZxsC6eHaRILgkCHv6xGrzl72DYYzBOKw/5dIHMyNtuWPqSaXgq9bntZo49HeOMMbivrMBPlQ91FVpr4vf+AZQidsvHwj6O6ABlGJhTxuNv2o4+VhP2cbo0f10Q/DDsHp75MaAfqqw/Xp4nvjQXfJdyzK5B/v1ENqmBpWAY6DwGP/xUv4+mSl4aU6ZJ8Z1fR40YQuLXj5xStt+VeOsqwfd7TLNTkOBHj+X/f/buPLyt8kr8+Pe9V5Jt2Y5jx46zx1kcZSEJa1lLCFshUJayDl3oBrR0mU6hpUzpTEuZmTJDO0xbpi10o/xKgaGFsgRKCiGsYUmAbERxFmd3YsdbbNmW7r3v7w9ZjpPItmxfrT6f58lDciW/74lQbOnovOds3ArtHWkbcXsk7znRaS5W99SX0ItvgWVlxJGXGM9J0b4fudj0VDe1wqiirB7Xmk7eixeDZWP97bV0hzIo1stv46zfjOeCj2J2l7qL7BN70WKvr0lzJNmtp/JjhI657c2YOQW9qw4dSl0vI/P4uYO6LjKL2cfUQPn/J4ZCeT2oMaNx9qfu2EtPs9MJA4/WVaOL8f/PHZCfR8ftP8betivZ4SWFvaa738f8kdHvAyT5MWJZsSMvJ85PcyRRxuTxGIFpWG++h24L0f589E2k95zMSX4YEytRk8Zhvbs2J8749aabWjDk05kh8y5ZBEpl1dGXw6o+vnxdusMRw2AeUw1I09PhcjZujSaBxw/8wjfXmTOrgNQefVH5edHfeL3R//q8eM4/HbO6KmUxiKFT5aXR3+T5wDBQ5aXy/08MixpXHu3FpHVK9nPq6qPP3e4jNwMxZ0+n4M6vQ3sHHV/7Ibo9lOQI3Xdo0otUfogclynNTnvznHMqRCwiL60k9Pc3UeMqMObMSHdYh/F8ZAEcbMcJbkt3KK7RjoNubpVmp8NgjCvHPPEY7HfX9ZRNZjrrpZU4GzbjufBMzJkjo8lVruqp/JCmp0Om2ztwduzFnD0dpVS6w0k7o/t7gr05dUdfYsk77+XnYowqBNPs6T8iMp/eVQeA57TjKL3lenzXLJHEhxgWY+wYCEd6JhImk45Y6Pom1NgylCfxKmjvxYvxfeYynK076fjnn6QsUeMWe+0mVFkJasLYdIeSMpL8GIG042CvWo+aMBYjg57s3u6Rt10//QNOSxues0/OuBehPX0/cunoS2s72I40Ox2mnsanS1ekN5AEHF718Q/pDkcMkxpXDkWFWMvfYsu4s2i77OaseB5mErumFrQe8c1OY2ITX5wU9f3QLQdxtu+JfuhRUYY5vgI6OqG1LSX7i+FzupMfxqRxaY5E5ApV2T3xpS75fT/0vgZwHIwB+n3Ek3fL5zFPPAZr2RuEf/N4EqJLDqehCb1nP+b8WRn3fiuZ5ID/CORs2YlubsV75onpDuUwRmAalJWg90RnbFsr3iFy/IqMmjrh6U5+2G9/AJ/7RJqjcYfTJGNu3eA9/ww67/oFkWdeJu8LV6Y7nH5ZL76Js3ErnovOwhwho81ymfXcK9B2qNmps6mWjlvvBsio75+ZzNnY3exU+n0AYHZXXKSq8sNeVxNNPnUf4fJMGEskWIuzrwFTGnFnPO04OLv3QUkxalRRusMROcKo7J74sv8A5pwZLF++LO79/H4foVD4sGuLF583qL16mp0O4dij8noo+MnttF/5dbrufRBz7gw8px0/6HVSLVYtOpKanYJUfoxI9qpYv4/MOfIC3S/ge41E07v30XHr3Rn1CaYxrhxjygSsd9ehrdzo+9Ez6UUqP4ZFjS7G89ETcYJbUz4lYTC049B138NgGFL1kSO67n80/vUHHktxJNnL7j7KaI7wSS8xalQRqnIMTgqSHzpiYX+4Bfz5PcdtPN0NB3Vd6podiqHT+xshHMGYlBnT+URuSGXlh1PX3ex0iD2fjPJSCv7nu2AYdNxydzQZmOFiRw1HUrNTkOTHiGS9sxbIvORHtryAN09eAO0dOBs2pzsUV+hGSX64xXtx9FP2yNKX0xtIP6wX38QJbsW7ZBHm9MnpDke4oK+mlM7m1DWrzHbOxq3gMXvefAswZk5F1zUkfYSyU1MLXWHMuTNRpgmAOXYMGAZOCsdciqE7dORlfJojEbmkd+VHMmnbRtc1oEpHoQryh7yOZ+Fs8u/4MrrlIKGv34Xu7HIxSvfFkh9Gd8XdSCHJjxFGa4397jrUmFKMqRPTHc5h+nwBn8Ju84mIHX2x3smNvh+6KdpISsm0l2HznHUy+AuIPLsiI5teaceh63+jVR8+qfrIGUZfR5dsm9C37sbuPtIh4tO2jb2pFmP6ZJTPm+5wMkYqjr5oraMvwA2FOe/QC3DlMVEVpeiGJrRlJW1/4Q5nVx0oJZUfwlWqO/mR7MoPZ1MthCOuTPryXnUB3ivOx/lwC50/+HlGvhaE7u+9a4KoyeMwRtiHn5L8GGH0jr3o+kbMk47JuOY2fb2A7/OFfZrkWtNTLT0/XKMK8vGeeyp6Vx32BxvTHc5RrL+/gRPchveiRZjTJqU7HOGSvBuviXtdjR+L9ewK2j/xVUI3/Ut0THeGvhBLJ2fnXujolGanR4hNWklmBZGuq0c3NGFMm4wq8h++f2U5OA66vjFp+4vh0xELXVePKi89NK5YCBcYY6PHXpJd+WGv3hDdbwjNTo+klCL/jpsxjqkm8tcXcTJ0CpvesRda20Zcvw9IcvIjEAhMCgQCTwQCgdcDgcCrgUBgyhG3LwoEAm8FAoG3A4HARcmMRURZGTjiNqavF/B5N1yd4kj6Z1SUYUybhL1qPTqS/Z9IxZIfIy3zmyw9U1+eeTmtcRwp2uvjj1L1kYO8SxZRcM9t0abRHhMjMI2Ce26j6O+/o+CXP4h2oX/1XUKfuY3Qp24lsvwttOOkO+yM4WyM9vswZmfWaPV0M3smvtQmbY/+zpyrcd1vfKTvR0bTe+vBdmTKi3Cd8ufDqKKkV35Yq9cDYIx3ZwKmyvPhv/e7qNJRWK+txtm735V13XSo2enI6vcBya/8aAfuDgaDpwP/D7j1iNt/BlwOLAF+HAgEpBIlyTK12Sn0/QI+E6cVmB9ZAB2d2Otr0h3KsMmxF3eZpxyLKivBev6VjCrXtpa9gVOzHe/HF2NWSdVHrvEuWUTRE/cxY+/LFD1xH94li1BK4T3zJAr/8J/4/3gPnsUnY7/3IR1f+QHtl32F8FMv5kQCd7hix4Kk8uNwsapLO0mVH059I86Wnagxo1ETjn7TYcSaHUrfj4zm7NoLgDFZkh/CfUblmKRWfmitsVevB38BlLg3qciYMJaCn9wOWhN5/jV0e4dra7thpDY7hSQnP4LBYFMwGFzZ/cc9wOjYbYFAYCrQFgwG9wSDwQagFsi8d+Q5xnp3HYwq6ilnzTTxXsBnokMjb7P/6IsTa3g6Wo69uEF5PRizp6MbW9g68RzaLrs57ROLor0+/gimge+ma9Mai0gPz3Fz8d/3rxT+9X/xXnI2zraddH7nx7Rd+EXCf3wa3dGZ7hDTxgnKmNt4VGEBamIlTpKmV0Ueew4cB/OYWfGP4RYXgr9Amp5mOGfXPjANlAtHBoQ4kqosh4PtSUse6D370fsOYIwvd70dgOfkhZinHgehDiJ/exVtZ86USHttEEwDc87Iq3hMZaXFFcDTvf5cCfT+iVbffU0kibO3Hr2rDs8Jx6AMKbIZjp6+HzmQ/NDNrZDnA//QO1yLQyJLV2C/8V70D46Ds6k27SObrb+91l31cTZmVWY1OhapZVZXUfCjWyl6/jd4r/s4+kAznf/2C9rO/Sxdv/wTuuVgukNMOTu4DTV2DIZUvx3FnDkFfaDJ9eSYjliEH3sO8nwYs6ri3kcpFZ320BZCt4Vc3V+4Q3d0ohuaUOMrUF5PusMROSjZE19i/T6Slbwzj52NUT0Vvbce6/XVSdljsHTEwt6wBaO6aljTbbJVSr5TBQKBJcAk4PFel71A7xSbAnz9rVNa6sfjMRPet6KieBBRJi5Z6yZz7YqKYg6ueJM2oOSsExjt0j7Z+lgMf5FiugJVWO9/SHlJvnvr9rVdEh+LUMtBjPLRjB3rXuVHtj4Wbtj52/+Le93+3eNMuP5iV/aISSRm7TjsvP8RME3G3/4FvAl8TaY/xqlcO9vWTXjtimL4n29j3XEDrb/+My2/+TNdP32I8G8eZ9RnL2X0l67BM66cg0/8neZ7H2JLcDu+wFRGf+PTFF9+burjTdK6dmMLrXUN+M85ZVBxDDdmvz/+y5141wezl9vrHlgwi+YV75AXCuEZE/9nxJFrJ7Ju25MvcbC+kbwLFuAfXdjnup2Tx9GxbRfepmZ8Y0en9bEYaN14tyVrXbfWHu663oZGwlqTP20i+UesNdIei2x8XvQl7T+femmcNoEmYFRXKCmPcf2Hm+gA/NMn4omz/nAf48LCPPRFZ3Lwj89gr92ENhX27v00HWjBHFNC/ikL8HVXHabq+da1ZhMHu8IUfeSYuHsm699Ib4FAYDTwJPByMBj8fiAQ+B5wHXAA2B8MBj8xpIUTkPTkRyAQmA78F3BuMBjs3WZ+HzCm15/Lgbr+1mpqSjzzX1FRTH29+59gJWvdZK4dW7fjpXcB6JxT7co+2fxYuOKEY9DBWupeWsX4C07J2sfCamjCqJrk2h4j/XkRDtb2ed3N2BONOfLcCiLBWryXnUtzcQkM8DXZ8Binau1sW3doa3vgi9dQ+A8fJ/zoc4QffIKW+x6h5f7HMY+fi91rqlV4w1b23/gDDrZ2unYkMd2PsbUy+vezpk9JOA43Yg6Fwkdd8/t9ca8PZi+31w1PjPZx6NjTgDmmLKG1E1m3/ZePAeDMntFvzE5Z9LR05446rMkT0vpY9LduX2sna1031nZj3Y4tOwGwKisOW2skPhbZ+LyIJ7N+PkG4OJp0bQruJDQqfqPu4TzGba+/DwV5dBUWET5iDTefF8b5Z2A/+hzh94M9t9n1TbQ/vYKuLguzuiplz7fwK9Hq5HD1tLh7DvffyEAJkUAg4CF6GmR9r8tjgK8Hg8Fl/X6xC5I97aUIeBT4fDAY3Nt97c5AIHAJsAUoDwQCEwKBQDkwC1ibzHhGOnvVOvAXSEd7l/T0/Xgne4++6I5O6OiSZqcu6nNk8/TJKY4EtG3Tdd/DYBrkfUkmvIi+qUI/eZ+/gqJlvyP/zq9jTBh7WOKjt64HHktxdMnT0+x0tjQ7jcecWQWA09js2pr2xq3Yq9ZjnnECxgC9ptTYMaCU9P3IUM6ufZDnQ1UcnRgTwg1qbPRz8mQ0PtYtB3FqtmMumI0yk9sOwBg9Cgrij4KOHb1JlXQ3Ow0GgxbwCWBlr8tlHN4OI2mSXfnxVWAa0UkuAF3AZqA4GAzqQCDwJeCv3ff9x2AwGD+N1U2OvQxv3Yq3Hk7KusmS8f//Pnlh9Jfb68aRrLXHTqlgbP2rrq+bjY+FW+tWvP6QK+sktFciMQ/h332mP8apXDvb1nVl7S9fFf2VIsON95lnnkn4vhdffMTRs29dH/01SCPl2AuLjoX6V/mwn8d40OXmFQuh++fOugHX9VF462cTX7ufuPq7nuvHG5L1WJTeeGXCe+b6Y5GNz4thfe8cokF/77zy7OgvwO/m9yGAiuKe18B9fS9y83nh/9LV/YeTqufbr74X/TXItd089hIMBuu7cwMxGvh5IBBQwCPBYPCnQ1o4AUlNfgSDwR8BP+rn9peAkxJdT469DH3dPX98jo5v/Dt5/3g9eTdd49q62fhYuLlu22U342zfw/Qtz9HQ2uXaur0l87GoW/Ee7Vd+Hd+nLyX/9ptcW3ekPy8iS1fQ9cBjOFt2YEybhI5E0LV78H3yEvL++SZXOooPFLO2bdovvRlnxx6Knr0fY/J4V9YdKnleJH9dt9duu+xmnE21R9/g9YDjgH10CbIqHYUxZQLGlPEYUydG/ztlAsaUCajRh14kRZauoOv+R3G27MSYMZm8G68Z8lGa4ZTotl3+FZzteyh+53GUmdiHKyPp2AtA24VfpGmMie/zVxz1vWuw5ea65SAHF38GVVFK0dIHEirdjrz8Ns76GrxXXpD2x6KvdftaO1OONyTjsQg//jxNjzyC58wTMecf9iZmxD0Wfa3b19q5/lj0ZSjfO53mVtpOuxbP2acQuuLUuPcZ6mPc+d+/J/zAY/gfuItQOPnPi/Ajz6IPHF1Fp8pL8V2zJCXPN93ewcGTr8I8bg6FD/1Xwmu7eewlnmAweD1AIBAoBF4MBAKrgsHg64NeKAHSmnmEsN9dB4B5okwTdpPnIwsIb6ql8931MGtmusMZNB0bc1sqY27d5F2yCO+SRT0/6HXLQdo/823Cf3wKNaYkJUdQrOdewdm6E+8V5yec+BCit7wbr6Hj1ruPul7wH7fgOe90nN37cHbsQe/Yi7NjD872PTg79mCvr8H+YOPRC44qwpg6ATwmznsf9lyOTUQCUjreXIcjOFt2Ys6dkXDiYyQyZk6FvVsg1AmFBcNaK/zEMujswnftxQk/5sa4cpz1NUkpexdDZ735PgBq0rg0RyJymSopBp83KUff7NXrwTAwF86Gd5LyPvsw5gnzsF44eh/z+LlJ3zvG/nBzdMT4EQnLTBEMBtsDgcAKYC4gyQ8xdNY76yDPl7bzXblKd79423P5P2JUTx3Wp5fp4PQkP6TnRzKpkmL8D9xF+3W30PXTh1ClJfiuWZK0/bRt0/WLP4HHlF4fYshi38t6qphmTCHvhqt7rptVE+OOTtaWjd67vycZ4vROjny4FSwr7n5dDzyW0u+fztadYFkYgekp2zMbGdXR5IdubEENI/mhHYfwn56BPB++y89L+OvUuHIA6fuRQbTjYL/1AcwsRQ3Qt0WI4VBKoSrL0fvcHXWrwxHstZswAtNQRX5X1+6LWV0FgP3uenRjMxgGnsUn91xPBXtNevt99CUQCIwNBoP7A4GACZwC/CVZe0nyYwSwmw/ibNqGedJ8lM+b7nByRmTpCiIPPhH9g9Zp+/RyOHRzK4A0PE0BY+wY/L++i9CnvkXnnfehRo/C+7EzkrKXtfQVnG278F51AcbEyqTsIUaGI6uYEqE8Jmry+O6KoxMOu03bNgcXXho9NnMEZ8sON0JOmDQ7TYw5cyq8Em16akwe+qf81qur0Dvr8F5x/mFHoAaiSoohzyfJjwzibKpFN7ZgTJrjyjFOIfpjVI7BXrUebTuuNSa119dAOILn+HmurJcos7oKs7oK9c4HdL69Lu7PwmSy10anzZgLMq7y438DgcBkQAFPBoPBt5K1kSQ/RoDOt9eA1pgnyJEXN3Xd/2j86yn+9HI45NhLaplVk/D/6k7aP/sdOr79n6hRRXhOPdbVPbRl0/WLh6NVHze6099HCLco08SYOSVuL5G+JiUlixOMJj+M2VL50R+jeioAuqllWOtEHn4aAN8/fHxQX6eUih592b4H50AzxpjRw4pDDF/syIsxSZLrIvlUZTloDaEOKC50Zc3YhBXz+DmurDdYeSfMo/PdDdjvf4gxZ0bKkoj22k2oshLUhLEp2a8/wWDw971+33f3ZJcld66PyAidb34AgEf6fbiqr08pnc2p/fRyOHRTd+WHHHtJGXNeNf6fRbtsh772Q+x1m1xdP7J0BU7tbryXny9VHyIj9ZWUy7uh/074buup/JhVldJ9s40xdSIYBvrA0JMfzvY9WK++i3n8XMy5Mwb99aoyevQlbi8ZkXL2yljyQ/p9iOQzKrvH3bYnPvhiID3Jj+NSW/kRYxT5MQJV6KZWnG27UrKn09CE3rMfc/6sEV2xJcmPEaDjzQ/AY0Yb+gjX9PkppePQefcDOHWZX6Ib+yRPklSWmDgAACAASURBVB+p5TnlWAr+69vQ2UXopn/BrnXnB5+2bMK/eBg8HvJuTO0bSSES5V2yiIJ7bsMITIPuEmZjQSC1zU61xtm4LTqFpjA1572zlfJ5UaOL0Y3NaK2HtEb4kegYSd91g6v6iDHGSfIjU+hwBOvdtRgzpsi/HZESseSnbnMn+aEdB/u9DaiJlT3fW9LBPDba6NR+/8MB7umO2IdtmdrsNFUk+ZHjdHsHXR8EMedVo/z56Q4np/R5pKC4kPCDT9B2/ufp+OefYKf4HPtg6KYWUApVUpTuUEYc7/lnkP8vX0E3tRL64h2unGePPPsyzvY9eD9xnlR9iIzmXbKIoifuY/relzFmVeFs2IxT35iy/XVdA7rlIIb0+0iIKiuBcASG8OZDhzoJ/2UZqrwUz7mnDW3/sWNAKew1kvxIN3vNRujocv3IphB9OVT50eHKes62XejmVjwpnLISj1FWglE1Eb23Hmfv/qTvZ6/NzGanqSbJjxwWWbqCtstuBsvG2bGXyNIV6Q4ppxz26aXHxAhMo+Ce2yh+5Y/k//AbGFPGE3ny77R//EuEvnIn1nsb0h3yUXRTK2p0sYx5TBPf1ReS9/XPoPfsJ3TD99DNic94P1K06uNP3VUf0utDZAelFL6rLwTLJvLEspTtawdjzU6l30ciYtWBsT5RgxF5ZjkcbMd79YVDbrqu8nyo0lHYazahLXtIawh3xPp9mKdI8kOkRqzyYyjJ13gO9ftIz5GX3szjuqs/3kt+9Ucs+WEcU530vTKZJD9yVGTpCjpuvRu9ex8Q/YS/49a7JQHistinlzP2vkzRE/fhXbII5fPiu+J8Cp/6BQU/vQNz4Wys5SsJffJW2j/1LSIr3h5y6bDbdGOLHHlJM99N1+D71CU4m7cTuvn76I7OIa0TeWY5zo49eK/8GEYGNLISIlHej58NBXmE/+95tJ2aN7bOxm0AMuY2Qaq7yajT2Dyor9NaE374afCY0STXcGKoLIeOTpzN24e1jhgee+X7YBp4Tpqf7lDECGHEjr241PPDfi+W/Ehv5QeAGl+BqizH2bYLe+vOpO2jtcZZuwk1eRzGCH/dL9NeclQuTCLJdsow8J57Gp5zTsVetY7wrx/HeuUdOr68HqN6Kr4vXIn3wkUob3r+GWrbjpZ9z5iclv1FlFKKvO/ciNPUivXsy3R8498p+Pm/DOp5EZ3w8ifwelLeNFKIRC1ffnRlh9/vIxQKE7noeJwNW/A89BDm1IksXnxeUmPpaXYakGMviYiNQx9s5Ye9ah3Oplo8F56JMXbMsGLo3fdDKnbSQ7eFsNcEMecHUC5N3RC5J973ejj0/b63RL7Xq/LSaNPlNneOvVir1sOoopRPGItHKYV53Bys518l/Ls/U/DDbyRlH72zDt1yEM/pxydl/WwilR85qs9JJBncfyJXKaXwnDgf/y9/QOGT9+H9+GKcrTvp/M6PabvgC3Q99CQ6NLRP+4fDaWoFraXyIwMow6Dg3/4J84wTsF59l847/hs9iNnvkadeRO/cG636GF+RxEiFSA5zXrQM11m/OSX7OcGtMKoIJf9eEqJKisE0Bp38CP+xe7ztEBudHhaDTHxJO+udtWA7mKcsTHcoYgRRHhNVXupK5YdT34jeuRfPcXNQRma8DTamTUKNLiby1EtJ631lrw0C0u8DJPmRs/rKZmZClnMkM2dNo+Dub1H0/G/wffISdFMrXf9xP23nXE/nz/8fTtPQRwkOlt0ok14yifJ58d/7XcyFs4k8vZyu//x1QsejdMSi61ePdFd9SK8PkZ2MsWNQY8twane71tG/L7q9A2fHXszZ00f0uL/BUIaBKi1BN7UkfGzT2deA9fc3MALTXSkvV6WjoLAAe01w2GuJoYmNuPWcelyaIxEjjaocA+2hYR8bz6R+HzHKMDAXzoGIRfihvyZlD2l2eogkP3JUXw0PpSQ+MxgTK8n/7pcoevH3+G6+DoDw/z5M2zmfpfPffomze19Pw9ot486i7bKbXe/XYjdEz26r0lGuriuGTvnz8f/iBxgzphD+w5OEH3hswK+JPPUSemcd3qsuSOvINiGGy5xXDVpjb0hu9YddUwtay9GJQVJlJRCxoLUtofuHH3sebAffJy92JcmkDANzfgBn685hNYcWQ2e9+T4U5GEunJ3uUMQIY1SWg+1AZ9ew1rFXrwcyK/kBYMyehhozmvCjS13rbdKbvTYIpoE5Z4bra2cbSX7kKO+SRZgnL4j+wTw0iUT6fWQWo7SE/K9+iqIXHyTv9ptQpaMI//Gp6JjcW+/G2VQLto2zqdb1hrX2ge7kR5lUfmQSNboY/wN3ocZX0HXvg4Qff77P+/ZUffi8ktgUWc+YORV8XuwPtyR1oofT3e/DkH4fgxKrEnQSOPqiwxEijz0Ho4rwXnSWazGYCwPAoRJukTpOfSPO5u2YJxwz5Kk9QgyV6u4ZNNzKQOu9DeD1YGbYxBPl8eD75CVwsJ3w//X9um8odMTC3rAFo7oKVZDv6trZSJIfOUw3H4T8PKbv+nvPJBKRmZQ/n7xPX0rR878h/0e3Qh/NLrsSqARIVOzYy0jv+pyJjHHl+H/9b6jSUXR+/+dElr0e936Rp15E76rDd9WFPd3QhchWyufFmFUFbSGsFW8nbZ+eZqdS+TEosYkvOoGJL9ay19EHmvB94nxXX2ybx86Jri99P1LOXvkBAB4ZcSvSIFbZOpyqCN3egfPhFsxjqlF5PrdCc43v2ougIJ/wH55EhyOurevUbIeuMOb8gGtrZjNJfuQoHerEqdmOOXcmyiNDfbKF8nrwXXI29DHu0c2GtY4ce8lo5rRJ+H95J+T56Lj1bqy3Pjjsdh2O0PXLaNWH74ar0hSlEO6KNT6NPPZc0vZwNm4Fjyk9sAbJGMTEl/DDT4NS0RfzLjIXdFd+SN+PlLPefA8Az6mS/BCpF6v8YBgTX+w1wWjD3gw78hKjRhfju/Jj6LoGIs+94tq6Pc1OF0i/D5DkR86yN9SA40hjmyzVZ8PaiZWu7dHT8FSOvWQsc/4s/D/7HmgIffXOw3ohHHz0OfTuffiuvnDYIySFyBRGeSlqXAXWa6twdtW5vr62beyaWozpk6V0f7BGFYHHM2Dyw96wGfu9D/F89ESMKeNdDcEoLcGYMgF7TXBQE7HE8GitsVa+jyodJcfFRFq4UfmRqf0+evN95jIwDcK/fXzYzV1jpNnp4ST5kaPsNd1P9AVS4pSN+mpY6+zZ71o2uKfnx2ip/MhkntOOo+A/vwWhTkI3fo+uB5+g7dIvU//N/wKlUNMnpztEIVxlzpsJWrt+7hnA2bkXOrrkyMsQKKVQZaPQTa39Jh7CDz8DgPe6i5MSh7kwAK1tONt2JWV9cTSndje6rgHz5IUZMx5UjCw9PT+GkfywYsmP4+a4ElMyGBMr8VxwJk7NdqxX33VlTXttEAryMKZLtSNI8iNnHSpxkuRHNvIuWUTBPbdFP2HxRBvWej97OXi9dNzyIzrvfXDYn3pJw9Ps4b3go+R/72Z0Ywtddz8QPb8JoDVdd97n+iQgIdLJmDkFRhUR+csyV889AzgfxpqdSvJjKFTZaLBtdEv8iS+6s4vIsy+jJo/Hc8YJSYkhNmnElr4fKSMjbkW6GZXDa3iqLRv7gyDG9MkYGf6hX97nrwAg/Ns/D3stHY7gbNnZ3QbBHPZ6uUCSHznKXrsJVVaCmjA23aGIIfIuWUTRE/cxY+/LFD1xHwXfvoHCP/0ENXk84fsfpeNrPxxW12v7QDMU5KPy81yMWiSL79qLehoOHsnNRrhCpJvyePBddi76QBPWSytdXdsObgOk2elQxSa+9NX01N6wBbrC+K67OGkVArGmp/YH0vcjVaw3Y8kP6fch0kMV5EOeD9qH1vPD2bQNQh2YJ2TukZcYc84MzNOPx357Tc+RlaHSDY3dbRDkw/AYSX7kIKehCb1nP+b8WSil0h2OcJFZPZWiR+/FPO04rOVv0X7tP2HX7h7SWvaBFml2mmV0c2vc6242whUiE3ivvhCA8KNLXV23Z8ztbOlbMBRqTN9NT7XjYK+vgYI8fJedm7QYjOoqyM/DXiOVH6mgbRvr7TWoSeMwJo1LdzhiBFNF/iF/6Getyvx+H73lfS5a/dH128eHtY6z7wAg/T56k+RHDuppbCNHXnKSGl2M/5d34rv+cpytO2m/9p+wXls1qDW01jiNzXLkJcv02QhXplaIHGNOn4z5kQXYb32AXetebwc7uBVVOUZGfA+RUdb3uFtn+x5obcN78dmokuKkxaC8Hsx5M3Fqtg/r/L9IjLNhC7S2SdWHSL/CAghHhnQcMtbs1HP8XLejSgrz1GMx5szAWvYGzo69Q15H7+9Ofsh7wh6S/MhBPf0+pMQpZymPSf5tN5D/H7dAZxehL/0rXb/9c+KdoUOd6M6wNDvNMn01ws274eoURyJE8vm6qz8ij7nT+FR3dKL3HcCUfh9DV+QHnzdu5Ye9LvrBiy9JjU57MxfOBscZdkm4GFjPiNtTJPkh0ksV+YHBNz3VWmOv3oCqKENlSfWSUoq8L1wJjkPXg38Z8jrOvgPSBuEIkvzIQT2VH8dIiVOu8116DoV/+E9UeSld9/yGztvuQXd2Dfh1seMThlR+ZJV4jXAL7rkN75JF6Q5NCNd5zj0NVVZC5Ill6K7wsNfTDdFqBUP6fQxZdOJLCbr5INq2e647Ta3oHXtRE8ZipmAUqvT9SJ1Yvw/zlIVpjkSMdKowmvygbXB9P/Tufej6Rszj5mZVOwDP+WegJlYS+csynAFGjMejQx1wsF3aIBxBkh85RmuNvXYTxpQJqNHJKzsVmcNcEKDw//4Hc+FsIs8sp/0z38apa+j3a2Kf2knPj+xzZCNcSXyIXKV8XryfOB/dchDrhdeHvZ5zoAkgJW/Oc5kqKwHHQTcf7LnmdFd9pOpceayEW/p+JJfu7MJ+bwPG7OlyVEykXSz5MdjKj54Rtydkx5GXGOUxybv+cugKE/7j04P+eid25EVOAhxGkh85xtm+G1rbMKSxzYhiVJThf/BuvJefh7Ouhvar/hHrvQ193t/pSX7IixkhRObyXXkB4E7jU90QTX5I5cfwqCP6fuhwBHvjVij0Y0ybnJIYjLFjUOMrsD/YmPhxTzFo9uoNEI7IiFuRGYoKgMGPu7VXRV8Pe7Kk2Wlv3k+cjyopJvKnZ9ChzkF9rZZmp3FJ8iPH2Guk2elIpXxe8u/6Bnm334RubiF0/XcI//lvce8bO/YiDU+FEJnMmDI+OvJv9Xrsmu3DWksfaIaCPIwp412KbmSKHZeMVRA6m7ZBOII5bybKTN3LSnPhbHRjC3pXXcr2HGmslTLiVmSOoVZ+2KvXQ0E+Rhb2e1L+fLzXfRzd3ErkiWWD+tpY5Yd8IH44SX7kGEcmvYxoSinyPn0p/vvvQhUW0Pm9/6Hjrl+gI9Zh9+s59iINT4UQGc53zRIAIo89N+Q1tG2jG1swq6tQpulWaCPSocqP6M8Re+0mMAzMuTNTGoe5cHZ0//fl6EuyWG++Bx5P1owHFbkt1vCU9sR7fujmgzhbdmAeOxvlyc7v/b5PfhzyfHT9/i9oyx74C4i2QdD7G6GkCENe6x9Gkh85xl4bBI+JKWW9I5rn1GMpfOxejOqpRB5+mtANd+A0HWqWpLt/L5UfQohM51n0EVRFGeGnXkR3DK7sN0Y3toDjyJEXN/jzIT8P3diMtWMvurEFY+YUVGFBSsOINT21PpDkRzLo5oM4G7ZgHjcH5c9PdzhCQJ4PPOagKj+s96NHXszjsqvfR29GWQney89D796Htey1xL6otQ06uzDGjklucFlIkh85RIcj2B9uwQhMQ+X50h2OSDNj8ngKH/4JnnNPw357De1XfyN6LpteyQ/p+SGEyHDK68F75cfgYDuR514Z0hqxfh/ywcDw9Ux8aWmj8511QHrOlJtzZoDXk7NNT+2aWsKPPEvTjx8k/Miz2DW1Kd3fevsD0FqOvIiMoZRCFfrRg5j2Yq/O3n4fveVdfzkYBl2/+XNCfY6c7n4fSpIfR5HkRw5xgtsgYsmRF9FDFRZQcO8/k/fVT6F376P9k7fQ8aNfEXk+mjnu+PpdRJauSHOUQgjRP98VHwPDGHLjU31Axty6yjRAayJbd0U/iW1tT3kIyufFnDMDZ+PWhEa8ZxO7phbrhdejz1vHQR9oxnrh9ZQmQHpG3EqzU5FJivzQ0XnYqO3+2KvXg2lgLszu90bG1Al4zj0NZ8Nm7JUfDHh/Hev3USnJjyNJ8iOH2Guj8+5lpJHoTRkGeTdfR8FP7wDbIfKHv0J3yaCzZQcdt94tCRAhREYzJozFc+aJOGs3YW/YMuivdw40gVKY1VXuBzfC2DW16J29moxaNtay1L4xjzEXzgbLxl6/OeV7J5O9an3866v7nuLmegwr34ciP+a86pTtKcRAVGEBaJ1Q3w/dFcZeuwlj9vSeZqnZLO8LVwLQ9bs/D3hfZ/8BMBSqvCzZYWUdT7oDEO6xY81OpauviMN77ml0jq9Ab99z1G1dDzyGd8miNEQV3/Ll8Tta+/0+QqHwYdcWLz4vFSEJIdLMe80SrJffJvzYUgq+/7WEv05rja5vQo0qSnlfilzU3xvzVCeXzIWz4aG/Yn+wEc8J2V3W3pvu1aPrsOuN8a+7zdm9D2f7HjyLT8naJpEiNx2a+NKBGlXU733tdTUQsbL+yEuMOX8W5kcWYL+2Cue8+RjlpXHvp20HXd+IKhuN8spb/SNJ5UcOsddugsICjGmT0h2KyFB9jQR0tuxIcSRCCDE4njNOQI2vIPLMy4MbddgWgq4wqo8XimJw0v3GvLeeiS851vejr35cqWpSbnWX1Uu/D5FxuhPYum3gnwH2e9nf7PRIvs9dARz6u8WjG5vBslFy5CUuSX7kCN3ahrN1J+Yxs1CG/G8V8RkzpgzquhBCZAplmviuugBCHUSeXp7w1zndzU5V+ehkhTaipPuN+WF7ThiLKi/FzrGJL2YfVSzGnBkp2d9a+V40jlMk+SEyS2zcbSIJcHt1tErNPD53kh+eM0/EqJ6Ks3k7urUt7n10d7NTmfQSn7xLzhH2uhoAaXYq+pV34zXxr99wdYojEUKIwfNe8TEwDcKPPpdQx3sAfSCW/JDKDzf09cY8HW8wlFKYC2ej9x3AqWtI+f7JYkwaF/2N1wOGEW3yCDi1uxJ+3g+V1hp75QeoijKMGZOTupcQg9XTu2OAyg/tOFirN6Amj8upJIBSCt/nrwRHY68Jxr2P093sVCo/4pPkR46Qfh8iEd4liyi45zaMwDTwmBiBaRTcc1tG9fsQQoi+GBVleM4+BSe4tc8XfkeKjbk1xkjyww1mdRWe80+PJpMMA1Veiuf809PWTLbn6Mv7H6Zl/2RwancDYJ44n9Jbrsf3mcswqiagd9bhdH/YlbS9a2rRB5rxnHosSqmk7iXEYPVUfoT6b3jqbNkJrW050++jN++FZ0KRH3vD5riTrvT+A+D19FmlN9JJF5Qc0TPpRSo/xAC8SxbhXbKIiopi6usPpjscIYQYFN81S7CWvUHksefwdL/x7Y9uaIb8vJ5Pz8XwmdVVmNVVcZtQpzyWY7uTHx9sxHvBR9Mai1tiyY9YDzelFJ6zTiH8p2ew3nwPY/L4pO0tI25FRvPng1ID9vw4dOQl95IfyufFXDAb+43V2Otq8Jx4TM9tOhxBN7agxldIG4Q+yKOSA7SOlj6pyjE5VdolhBBCHMk85VjU5PFEnnsF3dJ/AleHI+jWNlT5aPkUO0eZc6vBNHKm6am2bJyde1GjR2GUjuq5rgoL8Cw6CSIWkZfeRNt2Uva3V0aTH55TFiZlfSGGQxkGFBagBxh1m4vNTnsz582EPB/22iDaOvS9wN53ALSW94P9kORHDtB1DeiGJqn6EEIIkfOUYeC7+gLo7CL81Ev93lcfaI6+EJQjLzlL+fMxZk3DXr8ZHY6kO5xhc3bVQcTCqJp41G3GzKkYM6eg99YT/v0Tru+tbQfr7bUY0ydjVJa7vr4QblCFBdAe6rf/jbV6A6qkGGN6bk7AVD5vNAES6sQJbu25bnX3PlKS/OiTJD9yQM+Rl/mS/BBCCJH7vJefDx4Pkcf6b3wqzU5HBnPhbAhHcILb0h3KsDm1u4BDR156U0rhOfMk8BfQ9dM/YNdsd3Vvva8BOjplyovIaKrID7YDHUf3u4DoGFy9qw7z+Lk5ffTDXBCIVr29vxHtOABYe+sBMKTZaZ9y9xkxgkizUyGEECOJUVaC52On42zZge5+sRdPz5jbMTLmNpfFmp5aWd70VGuNU7sH8vNQ4+JXXqiCfDxnfQQiFh23/xgdsVzb39lVB8iRF5HZlL//cbdO98+EXBpxG48q9GMEpqGbW3v6BNl1DVCQD8WFaY4uc0nyIwfYazeBUpjzqtMdihBCCJESvquXAGCv73v6hW5ojk4kKZOu97msZ+JLlvf90PWN0B7CqJrQ7yfW5rRJeC87F2fDZsL3P+ra/s6ufWAYeD6ywLU1hXBdUQFAn01PdU/yI/eanR7JXDgHiPY40aEOnJY2jMoy6XHVD0l+ZDlt29jrajCmT+4Z/ySEEELkOvPEYzCmT45Wf3R0HnW7dhz0gWZUWQnKNNMQoUgVY+oEVEkx9geJjT/OVM627ikvUwfuU5B/+02ocRV0/eqRfhOAidLhCHpfA+Yx1ahRRcNeT4hkUYXd73f6qvyoqwefd0R8KGyUlWBMm4Sua8D+IJr8VWOlX09/ZNRtlnO27oRQhzQ7zQHLly+Lez3eKMHFi89LyrpurO3GukKI9EjW96FkUErhveZC+OuT2Bu34jmiq79uaQPLQpXLkZdcp5TCXDgb65V3cBqaMLK0x4tTuwtMA2PKwKNsVXEhBXd9g9AXv0vHd35M4eM/ReX5hr73nv3gOJinSb8PkdliH/bGq/zQ4Uh0CMQxs1A+b6pDSwvzuDk423Zhr45OuLE3bkWNLsasrkpvYBlKKj+ynL1G+n0IIYQYmXyXnAMeE2f95qMan+qefh/Z+UZYDI65MPohkL0mO6s/9MF2dEMTamJlwm/aPKcdh/e6i3G27KDrZw8Nb/9Yv49TJfkhMlus8iPeuFu9rwG0zvl+H70dlQRqbcN64XXsmtq0xJPpJPmR5XomvUjlhxBCiBFGlRRjzJyKbjmI3rXvsNtiyQ8Zczsy9PT9yNKmp7EpL2bV4EZz5n/z86jJ4wn/7i9Yq9cPff9ddeDxYB47Z8hrCJESsZ4fcY69OHu6+32ckPv9PmLsVfH/3ccqQcThJPmR5ew1QcjzYUhpkxBCiBEodq77yL4Hh8bcyrGXkcCcHwClsrbyw+6e1mBUTRzU1yl/PgX/8U0AOm7/SdxPwweiQx3R/jgTKoZ1dEaIVFAeD+TnQdvRz3Wnbj8AnhGUxNNNLfGvN8a/PtJJ8iOL6Y5OnJpazLkzUF5p3yKEEGLkUZVjUOWlONt2HfbGz2lohiI/qiA/jdGJVFHFhRgzpmCv3YS27HSHMyi6LYTetQ9VUYYawohKz/Hz8H3uE+ide+n8yW8H/fVOd9WUMbFy0F8rRDqowoKjKj+07aD3HUCVjUaVFKcpstRTpfGnmcmUs/gk+ZHF7A+3gO1EP+0QQgghRiClFOa8meA40Z+LgA51RkeGStXHiGIuDEBHJ87m7ekOZVCs11aB4wy66qO3vK99GmPmVCJ/ehbrjdWD+lqnu9+HMXngRqtCZAJV5IdwBB2O9FzTB5ogYqHGj6xpJ30d8RlJfU8GQ5IfWcxeK81OhRBCCGPWNPB6sDdsRjsOVn0jIM1OR5qevh/dIx+zhbV8JQDGtMH1++hN5fmix188Jh133ItubUvo67TWODvrID8PlaVTcsQIVHh03w9nb7TfhzF+bFpCShezugrP+adH//0aBqq8FM/5p8u0lz5I8iOLHUp+SOWHEEKIkUv5vBizquBgO87Ovdj7u5Mf8mZuRDEXRs/5Z1PyQ1s2kRXvRI9oDfP5as6rJu+ma9F1DXT+6P7E9m85CG3tGBMrUUoNa38hUiU28YVek0703mi/D2N8RTpCSiuzugrfNUsoveV6fNcskcRHPyT5kcXsNUHU6FGoyePSHYoQQgiRVubcmQA46zdj10vyYyQyZkyGIn9WJT/s9zZAaxtG1URXkg++G6/BmDuTyJN/J/LimwPePzbi1pgkryVF9lBFh4+71Vrj7G2AIj8MoW+OSK1AIDA6EAi8HAgEvt/95/GBQOClQCDwViAQ+F4y90568uPIv9wRt70YCARWBgKB15L9F801TmMLelcd5vxZkqkXQggx4hljx6DGjsGp3U1k+17welCjitIdlkghZRiY82dFm992dqU7nIRYL0WPvAx2xG1flNdDwX/cAj4vnd//GU4fkyBiDvX7kOSHyB6xyg8dq/xobYNQB8a4cnlflOECgYAHeBronaW+E/gZcApwXiAQSNqs4qQmP/r4y/VWBnw0GAyeEQwGf5jMWHJN7MiLIf0+hBBCCABURSloHX1BrMHZsiPdIYkUi/X90PsOpDmSgWmtsZa/Bf4C1CT3Jq2Y1VPJ+/pn0Aea6fzBfWit4+/vONFJL6OKor+EyBZFh/f8GKn9PrJRMBi0gE8AK3tdPgdYGgwGNbC0+89JkdTkRx9/ud48wWAw0sdtoh/22ugce+n3IYQQQoBdU4uzfvOhC5aF9cLr2DW1aYtJpF4s+eHsa0hzJANztu3C2bEHz+nHoUzT1bV911+GefxcrBdew6mJP/1GNzRBVxhjkvT7ENmlp+dHT/Ij2u9DjcB+H9koGAzWH3HJHwwGY+V69UDS5m57krVwTDAYrA8E+nyD7gsEAi93//6HwWDwxf7WKi314/Ek/sOhoiI5M56Tte6g1v7Bl6O/3F53kDLisciRdf1+X8K3DWavwazr1trJWjfeQNbQ9QAAIABJREFUbW79/8zU50U61h7uus8880zC97344ovTvm5/RtpjnMx/e0n/frFwVvRXPwazbjKfb6n+3pmp35OT8vPpynPgynP4sI//fxn1WFTMhfpXAVjbz/NtyDH/7VcAfT4WRVXj4VufG/y6/cTV121pf14MsHZGPS+GuK5ba2fFY+H3UdjruVty0Zlw0ZlDWjdeXP1dz7jHwsV1+1t7uI/FAHovro74s6uSnvzoTzAYDAAEAoFpwCuBQCAQDAZDA3zZiKe1pjZwMUZJEVPfeTTd4QghhBBp1/TjB8Fxjr7BMCi95frUByTSZsfJ/0BzCYz+2nWQwRUNuy+6mc5311O1/smk7dHy2ydoevD/4Z0+iaIrzjvstoP/9zes2j2UfOUfMPz5SYtBiGRo+fWf0eEIJZ+7jOaf/wlP1QSKr/pYusMSQ9MWCATyg8FgJ1AO1CVro7QmP2KCweC2QCCwB5gI1PR1v6amxPMiFRXF1NcfdCG61Kw7mLWdHXtxmloxTj0uofvn8mORS+uGQuG41/1+31G3DWavwazrxtrJWrevtd34/5nJz4tUr52s57I8L9xdNxO+Xwz275Ds54UqHYU+0HzU7aqspOc+I+WxSMW6fa2dET+fjpmF3ryOtt0NGGUl7q2Le4+F09hC5zvrMI+bS6P2JO2x0Befg3rp70S27uLguxt6piIV+EysXftQ5aV0YkAG/RuR1y1DW9eNtbPpsdD+AnRTK5Ed0ffJemz5kL7Xx1u7r3gHu3YmPC9S/VgMsRrkVeDCQCDwJLAE+OZQFklEykfdBgKBOwOBwCWBQCA/EAiM6r5WAYwFpDNZAg71+5Bmp0IIIQSAeUL85vDm8XNTHIlIt0NNTzO374e14m3QGs/ik5O6j1IK7+JTwOfFem01urUtuv+e/WDZGBOTdrReiKRShdGmp+HgNgAM6feRzb4NfJ1on9CXgsHgu8naKB2VH5VAcfevpYFAIAKYwFd7NToR/YhNepHkhxBCCBFlVlcBYK/egG5sQZWVYB4/t+e6GDnMhbPhz9Gmp+acGekOJy5r+VsAeM4+Jel7qeJCPB89EevFN4m8tBLvpedg1e6J3iYjbkWWUkXRpqeRrbvAUKjK8jRHJAYjGAz+vtfvdwOLU7FvSpIfR/zlbup100mp2D/X2GuCYBo9pYtCCCGEiCZAzOqqPkt0xchgzJoGHg+6LjMrP3RXGOv1VRhVEzGnTUrJnkZgGsbWnTjbdhF+8Alo74jeEOpMyf5CuK1n4kvEQlWOQXkzopuDyHApP/YihkdHLOwNmzFmTUPl56U7HCGEEEKIjKI8JmpsGbqxBR2OpDuco1grP4COLjyLk1/1EaOUQk2ZEP1DLPEBWC+tlHHQIjt1V34AGOPkyItIjCQ/soxTUwvhiBx5EUIIIYTog1FZDlqj9x9IdyhHsZavBEh6v48jOes2xb1ur96Q0jiEcINuPNTg2t66U5J4IiGS/Mgy9ppYs9NAmiMRQgghhMhMalz0/L+TYUdftONgvfw2avQozGPnpHbvppb41xvjXxciU9k1tdhvrTl04WA71guvSwJEDEiSH1mmJ/mxQCo/hBBCCCHiMbqbH2baxBdnw2b0/gN4Fp2E8pgp3VuVlsS/Xhb/uhCZyl61Pv51qWISA5DkR5ax124CfwHG9MnpDkUIIYQQIiOpwgIoLsTZ14DWOt3h9IgsfxtI/ZEXkHHQIndIFZMYKmmLm0V0Wwhn607Mk+ajzNR+WpBtli9fFvd6XxMAFi8+L9khiQyQjc+LwcQ8mHiTta44XLzHOZOfb0LkEmNcOU7Ndmhtg5LidIcDdPf78HrwnH58yveWcdAiV6jSEvSB5qOvSxWTGIAkP7KIvb4GtJZmp0IIIYQQA1CV5VCzHaeuATMDkh/Onv04G7dinnHCoTGdKSbjoEUuME+Yh/XC60dflyomMQBJfmQRe220S7ckP4QQQggh+mdUlmMDzr4GzMC0dIeD9fJbAHjTcORFiFwiVUxiqCT5kUUONTudneZIhBBCCCEym6ooBdPImKankeXR5IfnLEl+CDFcUsUkhkIanmYRe20QVVGGqhyT7lCEEEIIITKaMk1URRm6oRkdsdIai24LYb+1BmPODIzxFWmNRQghRipJfmQJZ18Det8BzPmzUEqlOxwhhBBCiIxnVJaD46DrG9Mah/XaKrCstEx5EUIIESXJjyxxqN9HIM2RCCGEEEJkh1i1rJPmoy/W8pUAeM8+Ja1xCCHESCbJjyzR0+9Dmp0KIYQQQiTEqCwHSGvfD23ZRFa8g6ocgzFnRtriEEKIkU6SH1lCJr0IIYQQQgxScSEUFuDUHUhbCPZ7G6C1Dc/iU+ToshBCpJEkP7KAdhzsdZswpk9GFRemOxwhhBBCiKyglIpWf7SHcA62pyUG66XuIy/S70MIIdJKkh9ZwNm2C9o7pOpDCCGEEGKQYn0/rD31Kd9ba421/C3wF2CevDDl+wshhDhEkh9ZQPp9CCGEEEIMjTEu2vfD2rM/5Xs723bh7NiD54zjUT5vyvcXQghxiCfdAYiB9fT7WJB7k16WL18W97rf7yMUCh92bfHi81IR0oDixRwvXsicmEXyyfMie2Xj9yEhROJ0a/S4S9e761HbdmOeMA+zuiole8emvHjOkiMvQgiRblL5kQXstUHwejBmTUt3KEIIIYQQWcOuqcV68c2eP+sDzVgvvI5dU5uS/a3lb4Fh4DnzpJTsJ4QQom+S/MhwuiuME9yGOWeGlEsKIYQQQgyCvWp9/OurNyR9b6exBfu9DzGPnYNRVpL0/YQQQvRPkh8Zzt64FSwbc37uHXkRQgghhEgm3dQS/3pj/Otusla8DVrjkSkvQgiREST5keHsNRuB3Oz3IYQQQgiRTKq0j4qLYn/S97aWvwWA5+xTkr6XEEKIgUnyI8PZa6LNTg2Z9CKEEEIIMSjmCfPi39DWgbM3edNftGVjvb4Ko2oi5rRJSdtHCCFE4iT5keGctZtgVBHG1AnpDkUIIYQQIquY1VV4zj8dVV4KhoEqL8VYOBu0Q+SZl3H2H0jKvs6uOujowrNYqj6EECJTyKjbDKabD+Ls2IN5+vEopdIdjhBCCCFE1jGrqzCrqw4bX21XjsFa9gaRp17CXvgRzNnTXd3Tqd0FIP0+hBAig0jlRwaz1wYBMOXIixBCCCGEa8zqqmgvjnCE0Be/i715h2tra61xavegRo/CPHaOa+sKIYQYHkl+ZDB7bbTfhzQ7FUIIIYRwlzl7Op5FJ6EbWwh94Z9xtu9xZV1d3wjtITyLTkJ5TFfWFEIIMXyS/MhgPZUfx0jlhxBCCCGE28x51eTdfhO6vpH2z92Os3vfsNd0tu0G5MiLEEJkGkl+ZCitNfbaTagJYzHKS9MdjhBCCCFETsr79KXkffNz6Lr6aAKkrmFY6zm1u8A08Jx+vEsRCiGEcIMkPzKU3rMf3dgi/T6EEEIIIZIs74tXkfeVT6J31RH6/O049Y1DWkcfbEc3NKEmVqIK/S5HKYQQYjgk+ZGh7DUbATAXzE5zJEIIIYQQuc9383X4vngVTu1uQl/4Lk5Ty6DXiE15MasmuR2eEEKIYZLkR4ay13Q3O5XKDyGEEEKIpFNKkfdPn8X3qUtwNm8n9MU70C0HB7WGXRvt92FUTUxGiEIIIYZBkh8Zyl67CQwDc+7MdIcihBBCCDEiKKXIu/0mvFddiPPhFkI3/Qu6LZTQ1+pwBL1rH6qiDFVcmORIhRBCDJYkPzKQtmzsDZsxqqei/PnpDkcIIYQQYsRQSpH/r1/Be+k52GuChL70r+hQ54Bf5+zYA44jVR9CCJGhJPmRgZyaWujskiMvQgghhBBpoAyD/B9+A8+FZ2KvXk/oq3eiO7v6/ZpYvw9jmvT7EEKITCTJjwxkr+3u97EgkOZIhBBCCCFGJuUxKfjRrXjOORV75ft0fOPf0bYd/86Og1O7B4r8qPLS1AYqhBAiIZL8yED22iAA5nxJfgghhBBCpIvyeij48XcwzzgB65V3sF54HW07R93P2r0fusIYVRNRSqUhUiGEEAOR5EeCIktX0HbZzWwZdxZtl91MZOmKpO1lr90EBXkYM6YkbQ8hhBBCCDEw5fPi/+kdmCcvxNm6E+vFN9DO4QmQ8OYdgIy4FUKITCbJjwRElq6g49a7cTbVgm3jbKql49a7k5IA0e0dOJt3YM6difKYrq8vhBBCCCEGR+Xn4b/vX1Hjx+LUbMda/hZaawC01kS27ASvFzWpMs2RCiGE6IskPxLQdf+j8a8/8Jjre9kbNoPjYC6Y7fraQgghhBBiaJQ/H+/FZ6Eqx+Bs3Iq14h201ujmVpymVowp41CmfHAlhBCZypPuALKBs2VH/Oub418fDntNrN/H4Ce9LF++LO51v99HKBQ+6vrixecNa2031hVCCCGEyBbK58V78WIif30RZ30NkbZ2dH0TAM6+A9g1tZjVVekNUgghRFxS+ZGAPntvOA5d9z+K7jo6ATBUPZNeZMytEEIIIUTGUfl5eC85Gwr96O17INQRvaEthPXC69g1tWmNTwghRHyS/EhA3o3XxL/Bn0/XvQ/SdsmXiby0sufs53DYa4OoMaNRE8YOey0hhBBCCOE+VZCP8nnj3mav3pDiaIQQQiRCkh8JME+aD6YBXg94TIzANAruuY3iFx/E95nL0Hv30/HVOwnd9C/YW3cOeR9r3wH03nrM+bNkTJoQQgghRAbTLa3xrze2pDgSIYQQiZDkRwLC/+8psB3yb7+JGXtfpuiJ+/AuWYQaVUT+d26k8C/3YZ52HPZrq2i/7GY6734AfbB90Pt0vfchAOaCgNt/BSGEEEII4SJVWhL/eln860IIIdJLkh8D0O0hwo88iyorwXvZuXHvY86cgv+Buyj42fdQ4yoIP/gEbUtuIPznF46aA9+fztXdyY/5kvwQQgghhMhk5gnz4l8/fm6KIxFCCJEISX4MIPz43+BgO75PXYLKz+vzfkopvOecStHTvyTvH69Hhzro/N69tF/7T1jvf5jQXj2VH/OqXYldCCGEEEIkh1ldhef801HlpWAYqPJSPOefLtNehBAiQ8mo237oiEX4wSehIA/vtRcl9DUqz0feTdfgvfQcOn/8W6xnXyZ03S14Lz2HvG9+DqOiLP5ejkPXex9iTJ2AGl3s5l9DCCGEEEIkgVldhVldhd/vIxRyb/qfEEII90nyox+R515B19Xj+9QlGKNHDeprjXHl+P/r21jXXkTnv/+SyF9fJLLsDfK+dC2+z1x2VIdwZ/senJY2vGee5OZfQQghhBBCCCGESLtAIKCAZmBt96UfBYPBZ1K1vyQ/+qC1Jvzbx8E08F1/+ZDX8Zwwj8LH7iXyl2V03ft7un7yOyKP/42822/Eu+gjPfez1wYB6fchhBBCCCGEECInlQDrgsHgGenYXHp+9MF+fTXOplo8F3wUY2LlsNZSponvqgsoWvoAvk9dgrO7jo4vfz86GnfbLiJLV9D5o/sB6Hror0SWrnDjryCEEEIIIYQQQmSKMqAhXZtnVeVHaakfj8c86vozzyReKXPxxRcndsfLz+IZ723R3781cDIioXUriuG/v8Uz58w5dG3/FhgD/PfNh9+3e8+E4wX8ft+gbquoSLy3SF9rJ2vdeLcla1231pbHIvnrxrtNHovMWre/teWx6P/6/2/v3qNlL8vDjn/n7IuAKLcDSCqKKPOIl4jY2lgkGqjGkogR4iVaDbq6UuNa0epaNm2qWQ1drY0tTXqjWTZqNFVXLDFqkCpRVMCQBMUYFHghXLyEIHcCHDiHs/f0j5nj3mez90H27GdmnpnvZ6293My4v/P6MPvnmffM/H6TsOZpmsWkznhf7VmbhcfkzXW3qu0s8rvr3ecsJqu7r7az2Pftj7Y9sACcEBEXA3cA72yt3biZ0GaU2vy4664d696+3gmmNjrx1G233fuIj7P07eu4/1Vv5+6f/nEWTz91y7qr19vr9Vi+8fvs/vylsM7lcDvbD2HxNac96u56tmrNo+pu1HYW+d2taDuL/O5G7UnobtR2FvvuPtq2sxhPd6O2s8jvbkXbWeR3N2o7i/zuVrSnZRaTfEzeqD0J3Y3aj2YWj7Qh0lprwJMBIuJM4P3ASx7VIoeQvvkREQcDnwK+3Fr7t2vuexHwPqAD/EZr7bPZ6/lR7PzgHwIwf0Leddo7nQ5zxx7Nbnrr3t+78560x5YkSZIkaYwuAP7bKB8w9ZwfETEP/DFwzQb/lf8OvBI4DTgnIsZ+DpLl79/C7s9fyrY4ls7RT0h/vM4hB61/+6Hr3y5JkiRJUjURcVhE7DmPxcnAX43y8VM3G1pru4EzgD9be19EPBm4r7V2c2vtduAm4FmZ6/lR7Py9P4LlZR7z5jPpdDrpjzf3vGeuf/uJee86kSRJkiRpxE4AvhYRlwK/Crx9lA+e/rGX1tptEetevvVI9j7T622D28Zm+a57eOiTF9I56nDmX3YyXPql9MecO+4YAJauuIrenffQOfQg5k58xg9vlyRJkiSputbaF4Hnjuvxx3nC0wX65/rYowNsfOpaNr7ay1adlfbOD58HD+7k0Le+loN/7JDRnfn3Od3+1zo8I3R+d6vaziK/u959zmKyuvtqO4t93z4Ja56mWUzqjPfVnrVZeEzeXHer2s4iv7vefc5isrr7ajuLfd++yau9jNU4Nz9+QP8ir3tsB27Z1w9kXu2l9+BO7nv/efD4A9n1shdz2233TvWZf7ei7Szyuxu1nUV+dyva0zILz5q+ue5GbWex7+6jbVeb8UbtWZyFx+TNdbei7Szyuxu1ncVkdTdqO4t9dzdqT/qGyMg3PyLibOBr9E+Euj0ifgzYBXSBK0e9nj0e+tQX6N31dyz+0mvoPHb/cS1DkiRJkiRtsXFcXeVI4HGttR7wFuDTwP8D3t5aW38bK1lvaYmdH/okLC6w+PqXj2MJkiRJkiQpyUje+dFa+71V3//zVd9fBPyDUaxhX3Z/4TJ63/tbFl71T9h2+KHjXo4kSZIkSdpC43jnx0Tp9Xrs/OB50Omw+KZXjns5kiRJkiRpi8385sfS177F8pXXMn/qC5g75onjXo4kSZIkSdpiM7/5sesD5wGw+OYzx7wSSZIkSZKUYaY3P5auu4ndF1/O3InPZP6E48e9HEmSJEmSlGCmNz92feiTgO/6kCRJkiRpms3s5sfyLbfz0PlfZtuxRzP/4uePezmSJEmSJCnJzG5+7Pr9T8Pu3Sy+6Qw622Z2DJIkSZIkTb2ZfNXfu/d+dn3iAjrbD2Hh5aeMezmSJEmSJCnRTG5+7PrEBXD/Ayy+4RV0FhfGvRxJkiRJkpRo5jY/erseYtfvfwYO2J/F15w27uVIkiRJkqRkM7f58dD5X6J36x0svupldB5/4LiXI0mSJEmSks3U5kev1+tf3nZ+jsU3/ty4lyNJkiRJkkZgpjY/lr/zNyxf/10WfubFbDvq8HEvR5IkSZIkjcBMbX4sfeNqABbfdMaYVyJJkiRJkkZlZjY/lm+5nd7NtzJ/8t9nrvuUcS9HkiRJkiSNyMxsfiz95VUALL75zDGvRJIkSZIkjdJMbH4s3/13LN/wfTpHHMbc83983MuRJEmSJEkjNBObH0t/eTX0esydcDydTmfcy5EkSZIkSSM09ZsfvR0PsHzNjXDQgWx76tHjXo4kSZIkSRqxqd/8WLryWlhaYv45T6ezber/50qSJEmSpDWmejegt+uh/ubHfo9h29OfOu7lSJIkSZKkMZjqzY+lq6+HnbuYe3aXzsL8uJcjSZIkSZLGYHo3P5aXWfrmNTA/z9yzu+NejSRJkiRJGpOp3fzY1W6Ce+9n29OfQmf//ca9HEmSJEmSNCZT91mQpetuYunr32bnHXcD0Dn48WNekSRJkiRJGqep2vxYuu4mdl/41b1vu/TrdA7Yj7njjhnPoiRJkiRJ0lhN1cdelr7+7fVvv+KqEa9EkiRJkiRNiqna/Ojddc/6t9+5/u2SJEmSJGn6TdXmR+eQg9a//dD1b5ckSZIkSdNvqjY/5p73zPVvP/EZI16JJEmSJEmaFFN1wtM9JzVduuIqenfeQ+fQg5g78Rme7FSSJEmSpBk2VZsf0N8AmTvuGA44YJEdO3aNezmSJEmSJGnMpupjL5IkSZIkSWu5+SFJkiRJkqaamx+SJEmSJGmqufkhSZIkSZKmmpsfkiRJkiRpqrn5IUmSJEmSppqbH5IkSZIkaaq5+SFJkiRJkqaamx+SJEmSJGmqufkhSZIkSZKmmpsfkiRJkiRpqs2PewGSJEmSJGn6RcQ7gNcCu4A3ttZuHNVj+84PSZIkSZKUKiKeCLwOOAn4DeB9o3x8Nz8kSZIkSVK2U4E/aa3tBr4I/KNRPribH5IkSZIkKduRwO0ArbUesBwRi6N68E6v1xvVY0mSJEmSpBkUEf8GeLC1ds7gn78PRGvt/lE8vu/8kCRJkiRJ2X4AHAYQER1gYVQbH+DmhyRJkiRJyncx8NKImKN//o/LR/ngXupWkiRJkiSlaq1dGxEfBy6jf6nbXxzl43vOD0mSJEmSNNX82IskSZIkSZpqbn5IkiRJkqSp5uaHJEmSJEmaam5+SJIkSZKkqVb+ai8RsQ04HTgYuLC1dvOq+97XWvuXm+wuAGfSPwvtp4B/CPwCcCNwbmtt5xBrfk5r7ZuD77cD7wGeCjTgN1trt26yuw14A/AS4HDgPuAq4GOttauHWG/KjAc/nzLnajNe1S7zXM6a8aBX6rns8eJhXY8XeLxY03XGKz9basaDXqnjhcfkh3U9JuPxYk3X48XKz3q82LubcryYFNPwzo+PAq8GngV8LiLeExGdwX0/MWT3FOBtwLn0L8NzGXA88P4hugC/s+r7c4ErgHfSv87xR4bongscBZwNfB64FrgJ+GhEvHqIbtaM97Qz5lxtxlDvuZw14z29Ss9ljxcrPF7s3fJ40eeM9+5WmvGeXqXjhcfkFR6T9255vOjzeLF31+NFX+bxYjL0er3SX91u909XfT/f7Xbf2+12L+x2u4d0u92Lh+heOvjP/bvd7t92u925Vff9+ZBr/ka32z148P3n1tx3yRDdi9f88wWD/zy42+1eMWkzzpxztRlnzrnajDPnXG3GmXOuNuPMOVebceacnbEzHuWcq804c87VZpw552ozzpxztRlnzrnajDPnXG3Gk/RV/mMvwK6IeEJr7ZbW2m7gX0fEK4GLgAOH6M5FxLbW2gMR8enW2tKq+zob/tSP5r3AlyPiXOCbEfFu4HzgFcA1Q3TvG+z2fQM4CdgB0Fq7OyKW9vmT+5Y1Y8ibc7UZQ73nctaMod5z2ePFCo8XKzxerHDGK6rNGOodLzwmr/CYvMLjxQqPFys8XqzIPF5MhGn42Muv0f+s0w+11v6I/ueg/nqI7oeBJw96b9lzY0QcA9y8wc/8SFprnwBeBzwJ6AI/CbwL+B7wy0Okz6L/lqT/CpwAvGXVfa8fops1Y0iac8EZQ7HncuKMod5z2ePFCo8XK87C48WetTnjFdVmDPWOFx6TV3hMXnEWHi/2rM3jxQqPFysyjxcTodPr9ca9hokWEc8CjgQWgVuAKwc7YVvVfgKwsJXtiHg2cAQJa86SNWdnvKLajAftUnP2eDEa1Z7LzvhhXWdMvRkP2qXm7DF5NKo9l53xw7oeL/B4MSvc/NhARJwGnEP/TL+303/b03bgOOBtrbULJq2dueYs1WbhjPO72e0MFWdRbcZQbxbOuG43U8VZVJtzxVlUmzHUm4Uzzu9mtzNUnEW1GU+S8uf8iIgvAevt4HSAXmvtlE2mfx14YWvtjjWPtx34LDDMkyqrndJNnDEUm0Vit+JzudzvSMEZZ7arzRiKzSKxW/G5XK3rjEfQLjjjzHa1GUOxWSR2Kz6Xy/2OFJxxZrvajCdG+c0P+id6eYj+JX+22nrzmWf4k99ktjO6mTOGWrPI7FZ8Llf7Hak448x2tRlDrVlkdis+l6t1nXF+u+KMM9vVZgy1ZpHZrfhcrvY7UnHGme1qM54I5Tc/WmvvjIj/Ady3dvdrSL8KXBgR97L324kOY++TykxSO6WbOGMoNovEbsXncrnfkYIzzmxXmzEUm0Vit+JzuVrXGY+gXXDGme1qM4Zis0jsVnwul/sdKTjjzHa1GU8Mz/nxCCLiSfRPfrMA/KC1dv2ktzPXnKXaLJxxfje7naHiLKrNGOrNwhnX7WaqOItqc644i2ozhnqzcMb53ex2hoqzqDbjidDr9abuq9vtfqBSt+KanYWzcBbOYtzdimt2FnW7FdfsLJyFs3AW4+5WXLOzGM0sxvG1bdybL0m6xbqZ7WrdzHa1bma7WjezXa2b2a7WzWxX62a27ea3q3Uz29W6me1q3cx2tW5mu1o3s12tm9mu1h2Lad38+ONi3cx2tW5mu1o3s12tm9mu1s1sV+tmtqt1M9t289vVupntat3MdrVuZrtaN7NdrZvZrtbNbFfrjsVUnPMjIo6nf2KW70XEAcAvAvcCH2utLQ/RfQpwCnAEcB9wFfCV1truLVhzSrtad9A+HjiV/mfWFoFbBu0rZqmb3VauiDgMeFxr7aY1t/9Ca+3jk9iu1h00jgJ2t9Zui4iD6R+XbtqC3+tS3ez2mse5sLX20q1sVuxmtSPiBcAzgL9qrV0+6d3MdmL354CnAa21tqV/kM9qV+hGxL8CPttau3JLFle0O4J21uuclG7FNfuaL787KcpvfkTEbwLPAQ4FPgT8PPAF4CDgCa21szbZfSvwMuB84KeBe+ifTOZE4LXDHNyy2tW6g/a7gZfQvx71baycrfg04AuttX83C93stnJFxLuAXwbuBOaAt7fWLh7cd3Fr7ScnrV2tO/j5XwPeABxC/xr3pwN/A7wA+Ehr7T/PQjd5zV8CVv/BoDNo/ilAa+2UWehmtiPivNbWbzbjAAAIKElEQVTazw++/xXgdcBXgJ8APtda+4+T1K245oj4amvtpMH359D/M+JlwHOBhdbaP9tMN7NdrTvoXQ9cATwR+Dj9F6C3b7ZXtZvZTnydk9KtuGZf8+V3J0n5S90CJ7XWXhgR88BfAL/bWjsX+n/QHqL7OuDk1lovIj4IfKK1dkZEPJv+tY9PnsB2tS7Az7TWXrD2xoh4H/0/YG72BX+1blo7Ih4AbgWW1tzVAXqttWMnqZvZTlzza4Fua213RDwN+N2IuKi1djbDX8c9q12tC/BK+n8rfDTwNSBaa3dFxH7AV4HNbiZU62a2L6T/Av+9wM30/539BfCmIdZasZvZPmLV968BTm2t7YiIOfr/7ja7SZHVzWxndR+36vvnrt6oiohLNtnMblfrAtzcWntV9N/t9wbg8xHxXeDDwPlD/E1xtW5mO+t1Tla34pp9zZffnRjTcM6PbRHxPOBngccCL4qI/QcHn/2G6HaAowbfPwk4GGCw4zVMN7NdrQuwIyJOj4iFPTdExCL9v8V8YIa6me2zgIuBp7XWjl319ZRhNigSuxXXvIvBC/vW2l/T/+jSIRFxHrD/EN3MdrUuwM7WWq+19t1+vt01eJwHgZ0z1E1rt9beC/wL4F30jz3fof/Rmu+01r4zK93k9vaIOCUiTqC/6bpj8HhrN2UnpZvZzup+JiL+T0R06b8IfSFARLwIuH9C29W6MHhnVGvtjtbab7fWngf8B/p/c3zNDHUz21mvc7K6Fdfsa7787sSYhnd+vIP+3wzcQ//tOWcANwE7gLcN0X0n8KnBL9RdwFtX3ffbQ3Qz29W6AK8HzgZ+KyL2p3+ei/uAS4B/OkPdtHZr7Q8iAuDxwN1DrjG9m9lOXPMHgJOALw8eZwl4R0ScBfyvCW1X6wJ8KyKOa61dR/8jYgBExHbgMTPUTW231m4AzoiINwKfo/+W16FV6ya2Pwa8nP5bt6/fc2NEHA58bwK7me2Ubmvt3RHxS8AHgeOBAyPiBrbgnTtZ7WrdgYe9m6/1z9Vy+eBdaLPSzWxnvc7J6ma2fc1Xtzsxyp/zY7XB26AWWmvD/g382u4CML/V3cx2ta4kPZKI6LTWHvZ/WtE/6dmBrbVbZ6Gb3V7TOwx4aRvyRLXVu9ltqaKI2N626JwZlbvZbUlbp/zmR0Q8GfhP9D+Xu0x/53UBuJz+ifZu3KLuNvrvlBmqm9mu1pUkSZIkaRSm4WMvHwHObq29evWNEfGP6Z9kaLNXFsjqZrardYmIX9/X/a1/ksSp72a2q3Uz29W6me1q3cx2tW5m225+u1o3s12tm9mu1s1sV+tmtqt1M9vVupntat1JMg2bH48BLlrn9ouAfz+B3cx2tS7ATwFXA38+ZKd6N7NdrZvZrtbNbFfrZrardTPbdvPb1bqZ7WrdzHa1bma7WjezXa2b2a7WzWxX606Madj8+APgzyLiT4Db6X/sZTv9E8AN87ncrG5mu1oX+pdr/AzwntbaHUO2Kncz29W6me1q3cx2tW5mu1o3s203v12tm9mu1s1sV+tmtqt1M9vVupntat3MdrXuxCh/zg+AiPh79C+neCT9DZ0fAJcMzoI/cd3MdrXuoD3XtuZSfKW7me1q3cx2tW5mu1o3s12tm9m2m9+u1s1sV+tmtqt1M9vVupntat3MdrVuZrtad1JMwzs/oH/ZyoMGXwvAg8DjJrib2a7WBehGxJ6NlUXgFuArrbUrZqyb2a7WzWxX62a2q3Uz29W6mW27+e1q3cx2tW5mu1o3s12tm9mu1s1sV+tmtqt1J8K2cS9gWBHxbuB3gAOAG4Br6f+L+q1HOmnLOLoV1zyiWTwWuHFN+z2z0q24ZmeR3624ZmeR36245mrdimt2Fvndimt2Fvndimt2FvndimvOnMXE6PV6pb+63e5lG9ze2ei+cXYrrtlZOAtn4SzG3a24ZmdRt1txzc7CWTgLZzHubsU1O4vRzGJSvsq/8wPYERGnR8TCnhsiYhE4HXhgAruZ7WrdzHa1bma7WjezXa2b2a7WzWxX62a27ea3q3Uz29W6me1q3cx2tW5mu1o3s12tm9mu1p0Y03DOj9cDZwP/JSIOoH9Z1nuBSwb3TVo3s12tm9mu1s1sV+tmtqt1M9vVupntat3Mtt38drVuZrtaN7NdrZvZrtbNbFfrZrardTPb1boTYxo2P+bp70R9FfhMa+0P99wR/UuzvmTCupntat3MdrVuZrtaN7NdrZvZrtbNbFfrZrbt5rerdTPb1bqZ7WrdzHa1bma7WjezXa2b2a7WnRjT8LGXjwLfAv4v8LMRcWFEHDG4b78J7Ga2q3Uz29W6me1q3cx2tW5mu1o3s12tm9m2m9+u1s1sV+tmtqt1M9vVupntat3MdrVuZrtad2JMwzs/5lpr/3vw/fkRcRrwxYh4M9CbwG5mu1o3s12tm9mu1s1sV+tmtqt1M9vVupltu/ntat3MdrVuZrtaN7NdrZvZrtbNbFfrZrardSfGNLzz49aIeP6ef2itXQC8AvifwNMnsJvZrtbNbFfrZrardTPb1bqZ7WrdzHa1bmbbbn67WjezXa2b2a7WzWxX62a2q3Uz29W6me1q3YkxDZsfv0L/GsQ/1Fq7Afgp4JwJ7Ga2q3Uz29W6me1q3cx2tW5mu1o3s12tm9m2m9+u1s1sV+tmtqt1M9vVupntat3MdrVuZrtad2J0er2peAeLJEmSJEnSuqbhnR+SJEmSJEkbcvNDkiRJkiRNNTc/JEmSJEnSVHPzQ5IkSZIkTTU3PyRJkiRJ0lT7/wgMdko3VC66AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "View_Overweight(hold_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们统计每期的优选基金超配股是否是沪深 300 或者中证 500 成分股。可以看到，平均 来看，六成以上的超配股为沪深 300 成分股，中证 500 成分股平均来看仅占 15%。2017 年以来 属于沪深 300 成分股的超配股比例一直处于高位，在 70%左右，反映出优选基金近三年的持仓风格越来越偏向于大盘股。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def View_Overweight(hold_df: pd.DataFrame):\n",
    "\n",
    "    hold_df = hold_df.copy()\n",
    "\n",
    "    start_date, end_date = min(\n",
    "        hold_df['period_end']), max(hold_df['period_end'])\n",
    "\n",
    "    # 格式化日期标签\n",
    "    hold_df['period_end'] = pd.to_datetime(hold_df['period_end'])\n",
    "    hold_df['period_end'] = hold_df['period_end'].dt.strftime('%Y-%m')\n",
    "\n",
    "    # 获取指数成分股字典\n",
    "    cons_dict = QueryIndexCons('000300.XSHG', start_date, end_date)\n",
    "    zz_dict = QueryIndexCons('000905.XSHG', start_date, end_date)\n",
    "\n",
    "    cons_dict.update(zz_dict)  # 合并字典\n",
    "\n",
    "    # 标记超配股票所属\n",
    "    df = hold_df.groupby('period_end').apply(\n",
    "        CreatOverWeightNum, cons_dict=cons_dict)\n",
    "\n",
    "    # 格式化数据 以备画图\n",
    "    df = df.reset_index(level=1, drop=True)\n",
    "    df = df.groupby(level='period_end')['is_cons'].value_counts(normalize=True)\n",
    "    df = df.to_frame('per')\n",
    "\n",
    "    df = df.unstack(level=1)\n",
    "    #df = df.swaplevel(axis=1)\n",
    "    df.columns = df.columns.levels[1]\n",
    "\n",
    "    idx = df.index  # X轴\n",
    "    # Y轴数据\n",
    "    p1 = df['000300.XSHG'].values\n",
    "    p2 = df['000905.XSHG'].values\n",
    "    p3 = df['其他'].values\n",
    "    \n",
    "    # df直接出图 pandas 1.1.0\n",
    "    #df.plot.bar(stacked=True,\n",
    "    #            figsize=(18,8),\n",
    "    #            title='优选基金组合超配股在沪深300和中证500指数中的分布',\n",
    "    #            color={\"000300.XSHG\":'Crimson',\n",
    "    #                 '000905.XSHG':'CornflowerBlue',\n",
    "    #                  '其他':'DimGray'})  \n",
    "    \n",
    "    # 绘制堆积百分比条形图\n",
    "    plt.figure(figsize=(18, 8))\n",
    "    plt.title('优选基金组合超配股在沪深300和中证500指数中的分布')\n",
    "    plt.bar(idx, p1, width=0.4, label='HS300', color='#DC143C')\n",
    "    plt.bar(idx, p2, width=0.4, bottom=p1, label='ZZ500', color='#6495ED')\n",
    "    plt.bar(idx, p3, width=0.4, bottom=p1 + p2, label='OTHER', color='#696969')\n",
    "    plt.legend()\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def CreatOverWeightNum(df: pd.DataFrame, cons_dict: dict) -> pd.DataFrame:\n",
    "    \n",
    "    '''获取超配股并标记所属指数'''\n",
    "    \n",
    "    # 超配股票\n",
    "    target_stocks = GetOverWeightStock(df)\n",
    "\n",
    "    is_cons = list(map(lambda x: cons_dict.get(x, '其他'), target_stocks))\n",
    "\n",
    "    return pd.DataFrame({'symbol': target_stocks,\n",
    "                         'is_cons': is_cons},\n",
    "                        )\n",
    "\n",
    "\n",
    "def QueryIndexCons(index_symbol: str, start_date: str, end_date: str) -> dict:\n",
    "    '''\n",
    "    获取指数成分股并做标记 \n",
    "    -------------\n",
    "        key:cons_code,values:index_symbol\n",
    "    '''\n",
    "\n",
    "    period = pd.date_range(start_date, end_date, freq='Q')\n",
    "\n",
    "    cons = []\n",
    "\n",
    "    for d in period:\n",
    "\n",
    "        cons_list = [i.split('.')[0]\n",
    "                     for i in get_index_stocks(index_symbol, date=d)]\n",
    "        cons += set(cons_list + cons)\n",
    "\n",
    "    cons = dict(zip(cons, [index_symbol] * len(cons)))\n",
    "\n",
    "    return cons\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "View_Overweight(hold_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 构造优选基金的超配组合\n",
    "\n",
    "## 组合构建方法\n",
    "\n",
    "每季度，我们将优选基金组合的超配股，按照等权的方法构成组合。考察该 组合在下个季度的收益情况。由于基金重仓股信息要等到每个季度结束之日起十五个工作日内才 能获得，因而此处我们在计算收益时将时间窗口均滞后 15 个交易日，避免引入未来信息。（例如\n",
    "2019/06/30 调仓时，考察的是 2019/07/19 — 2019/10/21 这 60 天中的组合收益情况）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def OffsetDate(end_date: str, count: int) -> datetime.date:\n",
    "    '''\n",
    "    end_date:为基准日期\n",
    "    count:为正则后推，负为前推\n",
    "    -----------\n",
    "    return datetime.date\n",
    "    '''\n",
    "\n",
    "    trade_date = get_trade_days(end_date=end_date, count=1)[0]\n",
    "    if count > 0:\n",
    "        # 将end_date转为交易日\n",
    "\n",
    "        trade_cal = get_all_trade_days().tolist()\n",
    "\n",
    "        trade_idx = trade_cal.index(trade_date)\n",
    "\n",
    "        return trade_cal[trade_idx + count]\n",
    "\n",
    "    elif count < 0:\n",
    "\n",
    "        return get_trade_days(end_date=trade_date, count=abs(count))[0]\n",
    "\n",
    "    else:\n",
    "\n",
    "        raise ValueError('别闹！')\n",
    "\n",
    "\n",
    "def VieAlgorithmNav(hold_df: pd.DataFrame) -> pd.DataFrame:\n",
    "    '''简单回测'''\n",
    "    #df_list = {}\n",
    "    df_list = []\n",
    "\n",
    "    for rptdate, df in hold_df.groupby('period_end'):\n",
    "\n",
    "        stocks = GetOverWeightStock(df)\n",
    "\n",
    "        # 格式化股票代码\n",
    "        # if len(code) >= 6 过滤港股 前十大中有港股数据\n",
    "        stocks = [normalize_code(code) for code in stocks if len(code) >= 6]\n",
    "\n",
    "        start_date = OffsetDate(rptdate.strftime('%Y-%m-%d'), 15)\n",
    "        end_date = OffsetDate(rptdate.strftime('%Y-%m-%d'), 75)\n",
    "\n",
    "        # 模拟季度调仓 季度持仓\n",
    "        #close_df = get_price(stocks,end_date=start_date,count=1,fields='close',panel=False)\n",
    "        #next_close = get_price(stocks,end_date=end_date,count=1,fields='close',panel=False)\n",
    "        #next_ret_ser = next_close.set_index('code')['close'] / close_df.set_index('code')['close'] - 1\n",
    "        #df_list[rptdate] = next_ret_ser\n",
    "\n",
    "        # 模拟季度调仓 日度持仓\n",
    "        ## 模拟逻辑：换仓时清仓后买入\n",
    "        close = get_price(stocks, start_date, end_date,\n",
    "                          fields='close', panel=False)\n",
    "        ret_df = pd.pivot_table(close, index='time',\n",
    "                                columns='code', values='close').pct_change()\n",
    "        ret_ser = ret_df.mean(axis=1)\n",
    "\n",
    "        df_list.append(ret_ser)\n",
    "\n",
    "    df = pd.concat(df_list).dropna()\n",
    "    #df.index.names = ['date','code']\n",
    "\n",
    "    # 通过调仓确定基准周期\n",
    "    begin = min(df.index)\n",
    "    end = max(df.index)\n",
    "\n",
    "    # 获取基准\n",
    "    benchmark = get_price(['000300.XSHG', '000905.XSHG'],\n",
    "                          begin, end, fields='close', panel=False)\n",
    "    benchmark = pd.pivot_table(\n",
    "        benchmark, index='time', columns='code', values='close')\n",
    "    benchmark.columns = ['HS300', 'ZZ500']\n",
    "    # 计算净值\n",
    "    AlgorithmNav = benchmark.iloc[:, :] / benchmark.iloc[0, :]\n",
    "    #AlgorithmNav = AlgorithmNav.resample('Q').last()\n",
    "    AlgorithmNav = AlgorithmNav.reindex(df.index)\n",
    "    AlgorithmNav['基金重仓股等权组合'] = (1+df.dropna()).cumprod()\n",
    "\n",
    "    return AlgorithmNav\n",
    "\n",
    "\n",
    "# 计算组合收益率分析:年化收益率、收益波动率、夏普比率、最大回撤\n",
    "def strategy_performance(nav_df: pd.DataFrame, benchmark_col: str) -> pd.DataFrame:\n",
    "\n",
    "    # part1:根据回测净值计算相关指标的数据准备（日度数据）\n",
    "    # nav_next=nav_df.shift(1)\n",
    "    # return_df=(nav_df-nav_next)/nav_next  #计算净值变化率，即为日收益率,包含组合与基准\n",
    "    return_df = nav_df.pct_change().dropna()  # 在计算净值变化率时，首日得到的是缺失值，需将其删除\n",
    "\n",
    "    analyze = pd.DataFrame()  # 用于存储计算的指标\n",
    "\n",
    "    # part2:计算年化收益率\n",
    "    cumulative = np.exp(np.log1p(return_df).cumsum())\n",
    "    # cum_return=np.exp(np.log1p(return_df).cumsum())-1   #计算整个回测期内的复利收益率\n",
    "    cum_return = cumulative-1\n",
    "    annual_return_df = (1+cum_return)**(252/len(return_df))-1  # 计算年化收益率\n",
    "    analyze['annual_return'] = annual_return_df.iloc[-1]  # 将年化收益率的Series赋值给数据框\n",
    "\n",
    "    # part3:计算收益波动率（以年为基准）\n",
    "    # return中的收益率为日收益率，所以计算波动率转化为年时，需要乘上np.sqrt(252)\n",
    "    analyze['return_volatility'] = return_df.std()*np.sqrt(252)\n",
    "\n",
    "    # part4:计算夏普比率\n",
    "    risk_free = 0\n",
    "    return_risk_adj = return_df-risk_free\n",
    "    analyze['sharpe_ratio'] = return_risk_adj.mean() / \\\n",
    "        np.std(return_risk_adj, ddof=1)\n",
    "\n",
    "    # prat5:计算最大回撤\n",
    "    # cumulative=np.exp(np.log1p(return_df).cumsum())*100  #计算累计收益率\n",
    "    cumulative = cumulative * 100\n",
    "    max_return = cumulative.cummax()  # 计算累计收益率的在各个时间段的最大值\n",
    "    analyze['max_drawdown'] = cumulative.sub(max_return).div(\n",
    "        max_return).min()  # 最大回撤一般小于0，越小，说明离1越远，各时间点与最大收益的差距越大\n",
    "\n",
    "    # part6:计算相对指标\n",
    "    analyze['relative_return'] = analyze['annual_return'] - \\\n",
    "        analyze.loc[benchmark_col, 'annual_return']  # 计算相对年化波动率\n",
    "    analyze['relative_volatility'] = analyze['return_volatility'] - \\\n",
    "        analyze.loc[benchmark_col, 'return_volatility']  # 计算相对波动\n",
    "    analyze['relative_drawdown'] = analyze['max_drawdown'] - \\\n",
    "        analyze.loc[benchmark_col, 'max_drawdown']  # 计算相对最大回撤\n",
    "\n",
    "    # part6:计算信息比率\n",
    "    return_diff = return_df.sub(\n",
    "        return_df[benchmark_col], axis=0).std()*np.sqrt(252)  # 计算策略与基准日收益差值的年化标准差\n",
    "    analyze['info_ratio'] = analyze['relative_return'].div(return_diff)\n",
    "\n",
    "    return analyze.T\n",
    "\n",
    "# 构建每年的收益表现函数\n",
    "\n",
    "\n",
    "def get_return_year(nav: pd.DataFrame, benchmark_col: str) -> pd.DataFrame:\n",
    "\n",
    "    result_dic = {}  # 用于存储每年计算的各项指标\n",
    "    for y, df in nav.groupby(pd.Grouper(level='time', freq='Y')):\n",
    "\n",
    "        result = strategy_performance(df, benchmark_col)\n",
    "\n",
    "        result_dic[y.strftime('%Y')] = result.iloc[:, -1]\n",
    "\n",
    "    result_df = pd.DataFrame(result_dic)\n",
    "\n",
    "    return result_df.T\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 收益情况\n",
    "\n",
    "从超额收益来看，优选基金超配组合相对HS300,ZZ500指数均能取得不错的超额收益。从 2008-2019年，相对于沪深 300、中证 500 指数均可以取得超过 10% 的年均超额收益。最近三年组合表现尤为突出，相对于全市场等权组合可以取得年均 30%的超额 收益，相对中证全指的超额收益为 23%，相对沪深 300 和中证 500 指数的超额收益分别为 16%和27%。 相比于优选基金前十大重仓股组合，收益实现了翻倍提升。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>基金重仓股等权组合</th>\n",
       "      <th>HS300</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>annual_return</th>\n",
       "      <td>0.230707</td>\n",
       "      <td>0.058897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>return_volatility</th>\n",
       "      <td>0.285303</td>\n",
       "      <td>0.245818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sharpe_ratio</th>\n",
       "      <td>0.054872</td>\n",
       "      <td>0.022451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_drawdown</th>\n",
       "      <td>-0.528165</td>\n",
       "      <td>-0.466961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relative_return</th>\n",
       "      <td>0.171810</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relative_volatility</th>\n",
       "      <td>0.039485</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relative_drawdown</th>\n",
       "      <td>-0.061205</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>info_ratio</th>\n",
       "      <td>0.928064</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     基金重仓股等权组合     HS300\n",
       "annual_return         0.230707  0.058897\n",
       "return_volatility     0.285303  0.245818\n",
       "sharpe_ratio          0.054872  0.022451\n",
       "max_drawdown         -0.528165 -0.466961\n",
       "relative_return       0.171810  0.000000\n",
       "relative_volatility   0.039485  0.000000\n",
       "relative_drawdown    -0.061205  0.000000\n",
       "info_ratio            0.928064       NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>annual_return</th>\n",
       "      <th>return_volatility</th>\n",
       "      <th>sharpe_ratio</th>\n",
       "      <th>max_drawdown</th>\n",
       "      <th>relative_return</th>\n",
       "      <th>relative_volatility</th>\n",
       "      <th>relative_drawdown</th>\n",
       "      <th>info_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.170221</td>\n",
       "      <td>0.335550</td>\n",
       "      <td>0.156226</td>\n",
       "      <td>-0.196102</td>\n",
       "      <td>0.307596</td>\n",
       "      <td>0.007275</td>\n",
       "      <td>0.056539</td>\n",
       "      <td>1.923579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.324883</td>\n",
       "      <td>0.239973</td>\n",
       "      <td>0.081421</td>\n",
       "      <td>-0.123665</td>\n",
       "      <td>0.445396</td>\n",
       "      <td>-0.013174</td>\n",
       "      <td>0.171334</td>\n",
       "      <td>2.815603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>-0.426252</td>\n",
       "      <td>0.225019</td>\n",
       "      <td>-0.148283</td>\n",
       "      <td>-0.451509</td>\n",
       "      <td>-0.148492</td>\n",
       "      <td>0.006886</td>\n",
       "      <td>-0.135082</td>\n",
       "      <td>-1.032105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.432759</td>\n",
       "      <td>0.251667</td>\n",
       "      <td>0.097940</td>\n",
       "      <td>-0.152696</td>\n",
       "      <td>0.330109</td>\n",
       "      <td>0.044279</td>\n",
       "      <td>0.071358</td>\n",
       "      <td>2.034115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.528172</td>\n",
       "      <td>0.281163</td>\n",
       "      <td>0.103923</td>\n",
       "      <td>-0.132133</td>\n",
       "      <td>0.609186</td>\n",
       "      <td>0.057941</td>\n",
       "      <td>0.089458</td>\n",
       "      <td>2.553970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.319603</td>\n",
       "      <td>0.261594</td>\n",
       "      <td>0.075062</td>\n",
       "      <td>-0.199351</td>\n",
       "      <td>-0.237405</td>\n",
       "      <td>0.069561</td>\n",
       "      <td>-0.102156</td>\n",
       "      <td>-0.933525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.492781</td>\n",
       "      <td>0.431422</td>\n",
       "      <td>0.072179</td>\n",
       "      <td>-0.515066</td>\n",
       "      <td>0.466858</td>\n",
       "      <td>0.034976</td>\n",
       "      <td>-0.080220</td>\n",
       "      <td>1.674258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>-0.123213</td>\n",
       "      <td>0.315326</td>\n",
       "      <td>-0.016273</td>\n",
       "      <td>-0.264211</td>\n",
       "      <td>-0.074752</td>\n",
       "      <td>0.101947</td>\n",
       "      <td>-0.070401</td>\n",
       "      <td>-0.380649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.229052</td>\n",
       "      <td>0.149947</td>\n",
       "      <td>0.091394</td>\n",
       "      <td>-0.090439</td>\n",
       "      <td>0.010664</td>\n",
       "      <td>0.046331</td>\n",
       "      <td>-0.029764</td>\n",
       "      <td>0.100619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-0.183117</td>\n",
       "      <td>0.286215</td>\n",
       "      <td>-0.035537</td>\n",
       "      <td>-0.333420</td>\n",
       "      <td>0.092463</td>\n",
       "      <td>0.066335</td>\n",
       "      <td>-0.014646</td>\n",
       "      <td>0.594667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.693314</td>\n",
       "      <td>0.246938</td>\n",
       "      <td>0.142229</td>\n",
       "      <td>-0.161104</td>\n",
       "      <td>0.291408</td>\n",
       "      <td>0.047674</td>\n",
       "      <td>-0.026190</td>\n",
       "      <td>1.926987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.476306</td>\n",
       "      <td>0.420132</td>\n",
       "      <td>0.071728</td>\n",
       "      <td>-0.225730</td>\n",
       "      <td>0.741955</td>\n",
       "      <td>0.135504</td>\n",
       "      <td>-0.064935</td>\n",
       "      <td>3.535960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      annual_return     ...      info_ratio\n",
       "2009       1.170221     ...        1.923579\n",
       "2010       0.324883     ...        2.815603\n",
       "2011      -0.426252     ...       -1.032105\n",
       "2012       0.432759     ...        2.034115\n",
       "2013       0.528172     ...        2.553970\n",
       "2014       0.319603     ...       -0.933525\n",
       "2015       0.492781     ...        1.674258\n",
       "2016      -0.123213     ...       -0.380649\n",
       "2017       0.229052     ...        0.100619\n",
       "2018      -0.183117     ...        0.594667\n",
       "2019       0.693314     ...        1.926987\n",
       "2020       0.476306     ...        3.535960\n",
       "\n",
       "[12 rows x 8 columns]"
      ]
     },
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    },
    {
     "data": {
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TmDp1TyKRCDfeeC1Lly5h9OgxXHrpVeTm5vLppx9z9913AA6/+tUMpk3bM23vU0RERERERKSvsjpg0J0PPnifxsZGZs58EIBQqJVAIMiZZ57H+PETqKhYw+mn/4apU/fk9ddfpaiomJkzH2TWrLt54YXnOPLIH3P77b/jllvuJBAIcsopJ7D77tPw+ZTQISIiIiIiIoODnmC7EImEaWsLJfZzc/Pw+/2MHz8BgG+//YZg0I21fPTRB4nsgWnT9uLjjz9k1aqV5OcXMHJkOaWlpYwZM44FC+YP/BsRERERERER6aeMyDB4/PV5fDh3zXq95q5bjuLH+07qsU9LSwunnTYjsV9dXeWeu+tuPPDAvZx++kmceuqZbLnl1gB88cUcbr31ZurqarnyyusS5wwbVgpAWVkZNTVVXtuwxHXLykqpqakCJq/PtygiIiIiIiKywWREwCBd8vPzueuuWYn9o446DHAzCmbOfJAnnvgb5557OmeeeR4HHngI2267PQ888Ci2PZe//OVhtt9+CpFIBMdxAHAch3A44rW1v47jQDgcGdD3JiIiIiIiIrIuMiJg8ON9J601G2CgBYNB/u//jmWbbbZl5sw/cuCBhySOGbMltm1TW1vL8OEjqK+vA0jsJ7clt4uIiIiIiIgMFqph0IWmpsbEdigUYuTIciorK2hrawNg+fJlNDU1UlxczJQpO/Huu28D8O67bzFlyk6MHz+B2tpaKisrqK2tZenSJWy+eWYFRERERERERER6khEZBpnm/fff5ZFHHqKgoIDc3FzOO+9iFiyYz1133UZBQSHhcJhLLrkSv9/P9OmHccMNVzNjxnGMGjWayy+/GsuyOP/8i7noonMBOPPMcwkGg2l+VyIiIiIiItIbDZ98TO74CeSMHp3uoaSV5SRPtt9AKioaNvyLSNqUlxdTUdGQ7mGI6F6UjKD7UDKF7kXJFLoXJVP09l4M19Sw8PyzAdjivoc28KgyQ3l5sdVVu6YkiIiIiIiIiHiiDfXpHkLGUMBARERERERExON4tetEAQMRERERERGRBCccTvcQMoYCBiIiIiIiIiKemDIMEhQwEBEREREREfHEmprSPYSMoYCBiIiIiIiIiCdSV5vuIWQMBQxEREREREREPJGaGgCsnJw0jyT9AukeQDq98MJz/POf/wCgpaWFpqZGRo4sTxz/9lubP/zhT0QiYa644mLGjRsPwDXX3EB5+Sg+/fRj7r77DsDhV7+awbRpexKJRLjxxmtZunQJo0eP4dJLryI3Nzcdb09ERERERET6KB4wCAwbluaRpF9WBwwOPfRwDj30cADuuedOysvLOeqoYwBYuHABv/3t1Wy99Ta89dabTJ9+GCeddGrK+bff/jtuueVOAoEgp5xyArvvPo3XX3+VoqJiZs58kFmz7uaFF57jyCN/PODvTURERERERPouUusGDHwFhWkeSfppSgJQWVnJO++8xeGHH5lomzXrbmbMOBnLsqivr6O0tDTlnFWrVpKfX8DIkeWUlpYyZsw4FiyYz0cffcC0aXsCMG3aXnz88YcD+l5ERERERESk/+IBAxwnvQPJABmRYfD0vBf4dM3n6/WaO47ajiMmHdqrvg89dC8/+9kvCAaDAHz55Rc0Nzex6667J/o8//xzvPHGv5g0yXDaaWdSXV3FsKQUlbKyUmpqqrz2Uq+tjJqaqvX4rkRERERERGRDcRyHSF2dux2Npnk06Zf1GQbLly/jq6++4KCDpifaZs68K2X6waGHHs6jjz7O3Xffj9/v429/e4xIJJIScHIcCIcjXrvjtTmEw5EBey8iIiIiIiLSf7HWVojFAIg2NqR5NOmXERkGR0w6tNfZAOvbvffew/HHz8Dnc2MnH3zwPkVFxWy99bad+lqWxW67TeOVV15kv/0OpL6+LnGstraW4cNHMHz4iER7vE1EREREREQyX6y5KbEdrasjUleX1cUPszrD4Ntvv2H16lXsuefegJsRcO+99/DrX5+c0q+mpjqxPWfOZ2y++STGj59AbW0tlZUV1NbWsnTpEjbffBJTpuzEu+++DcC7777FlCk7DdwbEhERERERkX6LNTen7Eeqs3uKea8yDIwxpcCzwBu2bV9ljBkLPAYUAi/Ytn3tBhzjBvPss09SWVnBySefAMBXX31BMBjk5puvT/T5xS+OZ968b/n3v18jNzeH0aPHctFFl2NZFueffzEXXXQuAGeeeS7BYJDp0w/jhhuuZsaM4xg1ajSXX351Wt6biIiIiIiI9E2sw5RyKzc3TSPJDGsNGBhjAsDzwJdJzdcAd+IGEd40xjxt2/aXXZ2fyc4//5Je9Zs6dQ+OPfa4Tu0777wr993355S2QCDA5ZcPyviJiIiIiIhIdutQ6NDyZXVS/tqnJNi2HQGOAN5Pat4PmG3btgPM9vZFREREREREBi0nmpph4HgFELNVr8Iltm1XdGgqsG075G1XAKPX66hEREREREREBlinpRSzPGDQ31UScpK2rQ77nZSVFRAI+Pv5UjIYlJcXp3sIIoDuRckMug8lU+helEyhe1EyxdruRX9hkOVJ+2WlBRRm8f3b34BBozEmz7btVmAksKqnzjU1zT0dlkGuvLyYigqtUSrpp3tRMoHuQ8kUuhclU+helEzRm3uxoco7blngOFRX1tNcNPTv3+4CKf2t4PAWcIgxxgKmA2/28zoiIiIiIiIimcGbkmAFgwAsue5qVt43M50jSqv+BgwuAM7ALYT4um3bH62/IYmIiIiIiIgMvHjRw3jAAKDh/ffSNZy06/WUBNu2H0raXg7ssyEGNFQ4jkNbW5v3L0R+fj4FBYWd+i1fvoxAIMDo0WPW6fX++9/32G23qYn9Dz98n1133b3f15s792u23HKrXvdvbW0lHA5TXJy983tERERERGRwi9TUAKkBg2zW3xoGQ8KyZUu5/vqrWLFiOeXlowh6N0Uo1EpVVRXjxo3nggsuZeLEzRLnvPzybObO/ZozzjgHy7JSrnfttVewdOkSHCdGVVUV+fn5jB49hvz8fL7znakcfvgRKf1Xr17FJZecz9ixY7nqqt+Sl5eXcvzAA/dm880nEY1GmTXrIW666Xq++uoLamtruPjiK9h992kAvPjiC9x6680YsyUAxxzzc6688mK22mobAKZPP4zp0w8DYMaM45g16yE++OB9Vq9uLz0xbFgp3/3u9wCorKxg1qy7ufXWO1m2bCmnnHIi48dPAKCmppq//e2ZlHHW19dz4YXnEItFueGGWygqKur7D0NERERERCTNql74BwCxlpY0jyQz9HdKwpAwYcJG3HPP/ey99z4cfPB0jjzyxxx55I/Zf/+D2XvvfbjnnvsTwYKWlhb+8IdbmDv3a0aPHs0555zG0qVLEtd67713WLFiOX6/n0AgSCAQIBaL0dLSQnV1NS+99E/efPP1RP+5c7/i8ssv4uqrf8uee+7NaafNYMWK5Snj23TTidxzz/2J/bq6Wu68cyY/+MGPEm3ffvsN//znP3juuZfIycnh4ouv4LHHHuLJJ19g880ncd11NyeCBcm++WYuzz//LFVVlaxZs5p//OPpxLGXX57NwQdP55VXXsJxHKZN25N77rmfe+65n5Ejy1Ou88knHzFjxgyOP979d/bZp/D+++/26+chIiIiIiKSTjljxgJQuO12aR5JZsjqDINkTU1NBALut6O5uSnR3tbWxuzZ/+CZZ57i5z//JQcccDAAO+20C9deewWjR4/huONOZOrUPQgE/CxfvgyATz/9mNzcPLbe2v2Uf/PNJ7PddjvQ1tbGY489zNtv/4drrrmB8eMnsOmmExkzZixnnnkyBxxwMD//+XEUFBSwaNFCTjttBuCm/FdUrKakpIRgMEhlZQVtbW1MnrwFv/71KcyZ8xlHHvkT3nnnP0yZsjO2/TVTp+5JRcVqSktLeeedt7j//pksWrSAE044ljPPPJf333+X4447ka+++oLW1lbAzRZ4++03OeOMc3n66SfYeuttiEajNDenrnSxZMki7r33T0QiEf7whz/g8xUAcMMNt3L77Tfz8MP3c9ZZ5yeyHkRERERERDJdoKyM0JLFBIePSPdQMkJGBAwqnvgbDR99uF6vWbzLrpQffUyv+7/zzlvk5OQA7sN5/EF/wYJ5rF69mksuuZIbbriGWbPupq0tRFnZCA477IeMHj0Gx3EAeO65p9lll++Qn19AMJhDTk4u+fkFVFdX8/rrr7Hddjvw6qsvEYvFmDp1D66++rKUMZx33sW8/vqrzJnzGbvvPo1NN53IXXfNYsaM41i4cD4HHHAwixcvYquttuGNN/7FjjvuzPjxE2hpaWb58qUALFy4kJqaKkaOHJm47uTJsMcee1FYWMi5557OxRdfwaRJk/H5fNTU1PDJJx+x0047A1BZuQawuOCCs7n33oeJRCJ8+OF/ufji87zjlQDU1dVx5JE/5s47b2PGjBlEIrH2731xMaeeeqamJoiIiIiIyKDiRLyih96zYbbLiIBBuixbtpRly5ayevUqfvSjoygrGw7AqlUree+9t3n//XcZO3YcJ510KgAPPfQX/v3v11i8eBHHHXdil9d8+eXZ+P0BKisr8Pv9LFq0gFAoxLbbbg/A97//AwCuv/4qLr30KjbZZFMA7rzzVkpKSrj44isAN2iRnGFQVFTM44//lebmZmpra5k/fx7jx0/gvffe4c9/fiDx+s3NzYTDbdTV1SXaGhsb2H//g3jttZcZNqyUW2+9ieuuu4lDDjmU5557infeeYtjjvk5AJttNonp0w/D52ufrbL77tO46KLLARLj2W67HQC4//5HtLauiIiIiIgMCU44DHQueuhEo1h+fzqGlFYZETAoP/qYPmUDrC8rV67g008/ZunSJcye/TwbbbQxfn+AJUsWs3z5Uj766AN23HFnnnzy73z11RcANDTU09bWxltvvQnAxhtvwpVXXgfA2LHjGT16LIFAgG++mUswmMPEiZsRCrVSXl7e7ThaWlpYvXoVZWXtaS+NjQ1MmbIjN998OzNmHMdGG23MgQcewkYbbUJBQWFiNYKpU/dg6tQ9Ol1zyZJFvPfeO/zkJz8DYN68b1mzZjUjR5Zz0kmnkZOTy/77H8RPf3oEP/zhkYnpGN98M5c//vF2pkzZidzcXLbccmveffdtTj75BMAtehg3e/bz3H//TEaOHJGSYVBZWcFll13Nrrvu1vcfioiIiIiISJo4kTBWINApOOBEIgoYZJtwOMyHH77PyJHlhEIhPvzwA4LBACUlw9hiiy15/fVXMWZLzj33Ql5+eTb77Xcgb731BosXL+KXvzyBe++9hxNOOAlwP9n/4os5iWtXVVXi9/tpaKgHwLbncvTRP02sxBDnOA4XXHAWo0ePYcyYMTQ3N1NQUEB1dRXDO8ybOfroYzjrrFOIRqPceefMRPu5555BTU01ubm5ibaWlhZqaqp5443XueaaG3jzzdc59dSzuP76q9hhhykAzJnzGYFAkG++sRPnbbbZJF544TVCoVa+/vorAKZN27NThkHcsccex69//auUDINZs+7u409CREREREQk/ZxwBCsQgA4r4jnhMCQ9b2WLrA4YbLnlVtx22x8ZNqwUgMcf/wvDh49g//0PAqDGW4MTYMWK5cye/Xzik/033vgXjY0N+L0oU0FBQcqKBk899XeKioo56KDp3b6+4zhYlpV4+G9tbeXss09l5swHse25jB8/gWg0imVZOI7DnDmfEQqFsCyL5cuXJ6ZQ3HTTrZx77hkcddSP2Wuv7wHuKgyzZz/POedcCJAIbMS9+upLvPDCc8ya9SD33HMnf/jDLZx22lnMnfsVt9xyIwD77XcgY7wqod155JGHePHF5ztlGOy44849niciIiIiIpJpnHAYKxgkFgqltEfq6/FnYY02K16wb0OqqGjY8C/ST3fddTtffvk54Bb0CwYDiQDCxImbcfzxJ3HhhWcTDrexZs1qSkqG0dbWRltbG8OHjyA3N5eLLrqcBQvm8fjjf01ct7a2Bp/PT0lJSaLt+ONnsMceewHug/arr75IXl5+4ngkEuaYY47lwAMP5qGH7mO77XbgwQfvZcyYscyf/y2bbz6Zk046lTVr1nCmpegWAAAgAElEQVTjjddw4okns/fe+wAQCoW8qRJv8OyzT9HQUM/RR/+UI444OuX9zphxHIccciirV6/iV7/6Nbm5uUQiEe6663YmT96CffbZn/r6esaMGQPACScc2+l7tscee3H88TOYPft52tpCXWYY7LjjzpqSIANO9TQkE+g+lEyhe1Eyhe5FyRS9uRcXXHguOFC4/Q7UvfF6yrEt7ntoA44uvcrLi62u2rM+YJDJotFoIoMhFGolNzcvcSwWixGLxRK1B9aHeMZDX+k/AckUuhclE+g+lEyhe1Eyhe5FyRS9uRfnnXEqgbIycsaMofHjj1KO9RQwCC1fxqr7ZjLmhBnkTthofQx3QHUXMPB11SiZwZ9UVCM5WADg8/nWa7AA6FewQEREREREZChwHIdYawu+vDyiDX0LclX87S+Eli5l9SMPb6DRpYcCBiIiIiIiIpL1nLY2iMXw5eczbO/vAZCzlppucZb3YW58WcahQgEDERERERERyXqx1hYAfHn5lOw2lUl/nEnRLrv26lwrJwfwgg5DiAIGIiIiIiIikvViLa0A+PLd6eC+3FyspGniPYkHDGJhBQxEREREREREhpR4hoE/aSU7fL17ZG5buRIAp01TEkRERERERESGlFiLNyUhvz1gYPl6l2EQWrQQAEcZBiIiIiIiIiJDS3INgzjLv/ZHZsdx2q+hGgYiIiIiIiIiQ0vHGgbuTnuGgROLdXletL4u6SJd9xmsFDAQERERERGRrBdt7WJKQlKGQXdLJoYrKxPb5cf8bAONLj0UMBAREREREZGs117DoKC9MTnDIBrp8rxwVXvAoGjKlA0zuDRRwEBERERERESyXqypCeghwyAS7fK8SFUVAKN+cRzBkeUbcIQDTwEDERERERERyXqRuloAAqWl7Y1WcsCg6ykJsVa39kHu2HEbbnBpooCBiIiIiIiIZL1IrRcwKBmWaMsd1x4E6C7DIF7bwAoGN+Do0kMBAxEREREREcl6kdpa/MXFWIFAoi1v4mbkjJ8AgBPpuoZBTAEDERERERERkaErUluLf1hpp/aCLbdyN7opepjIMAgoYCAiIiIiIiIy5DhtIXx5eZ3aWxctBKD+vXe7Pi+RYRDo8vhgpoCBiIiIiIiIZDUnFgPHSZmOENc6fx4ANa+9knqO4xBavoxYyC16qCkJIiIiIiIiIkNMvD6B5fd3Ojb6VycAUHbAQSntTf/7jMVXXkbTZ5+652pKgoiIiIiIiMjQkggYdJFhkDN2fLxXSnvz3K9S9n25uRtkbOmkgIGIiIiIiIhkt6i7ZGJXAQOfV5vACacWPax7498p+5Zv6D1eD713JCIiIiIiItINJxpl+R/voP7dd5Laup+SEA8idFxWsbtlFocSBQxEREREREQka4QrKmj69BNWPXBvoq29hkHnDIN4bYL4agjZRAEDERERkR40fvoJoaVL0z0MERFZT2KtrZ3anIg7JYFAFxkG3uoHTqT7gMHw6Yeun8FlGAUMRERERLrRtmolK/54B6sefiDdQxERkfUk2tTYqc3poYZBvC2WNAWh43SErs4bChQwEBEREelGpLYWgNCihWkeiYiIrC+x5uZObfHsgS5rGMQzDJKKHnYKOnRx3lCggIGIiIhIN7JxvqqIyFDXVYZBrKUFAF9+Qadj7UUP2/9PiDY0pPZRwEBEREQku8Ta2tI9BBERWc+SMwziUxFiLW6bv6uAgd8PPl9KEFkBAxEREZEsl/xpkuM4aRyJiIisL9GmpsR2PAgQbXIDBr6CzgEDcLMMkusWdAwYdFVIcShQwEBERESkG05ShoGjbAMRkSEhOWAQC7u/25vnfgVAcNSoLs+xAsGUgEGkMTVgEKmpXt/DzAgKGIiIiIh0Izn9ND6/VUREBrdYc2qGQSwUovGjDwmOGUP+5C26PMcKBrqsYRAviIhvaE5JGJprP4iIiIisB7HkgEFrC1CavsGIiMg6c2IxIvX17fvhMNHGRpxIhLxNJ2JZVpfnWcFgShA5tHQJAOPPPo+mTz9h+GE/2LADTxMFDERERES64DgOseS0VWUYiIgMeqsffpDWed8m9p1wGCcaA8CXm9vteVYgQKypvVhi26qVAORPmkzBFmYDjTb9FDAQERER6ULNSy9SPfuFxH5UAQMRkUGv/p23UvadcBgstzaBL6engEEwZUoC0RiBsjIs39Ce5a+AgYiIiEgXKp99KmVfGQYiIkNPLBwGbxUcay0ZBslFD51YFIZ4sABU9FBERESkS3kbb5Ky79YwEBGRwSpc376yQWD4cKC96CGALyen23N9Xg2DcE0NjXM+g1gMa4gWOkymDAMRERGRDhzHoXXhgpS2obrGtohItmhZsQKAvM02p2jnXah84u9uDYOYW8MgseJBF6JNjQAsvOCcREZCcMyYDTzi9FPAQERERKQDp62tc2M0OvADERGR9aZp4UIAhu31XRzvoT/W0owVcAMFlr/7jIE2L9gQDxa4/Yf+47SmJIiIiIh0EE9PBfAVFQEkqmiLiMjg1LpyFQA54zciUFwCQKS+Hifq1SboYwBgqBc8BAUMRERERDqJhdqnH4z99W8AqPvPv9M1HBERWQ/iRQt9wSD+EjdgEG1oSASELX8fH497yEgYKhQwEBEREenAaXUzDEr33S+RohquqEjnkEREZB3FIt7UMr+/PWBQX5eYctbTlISuggPKMBARERHJQrE2N2Bg5eRmxbJZIiLZID71wPL7k6YkNOBE2wMJ3dn4kss7tfUYYBgi9D+giIiISAfxtFUrEMiKPwhFRLJB++92P768PKycHKL1dTgxL8Ogh2USczsstQtoSoKIiIhINkoNGAz9KtgiItnAicSnHri/1/0lJUQbGno1JcGyLMoOOCi1LQsy0Ib+OxQRERHpIyea9Eelz2pvT1pOS0REBpdwfT3QHhgIlJQQbahPBInXljFg5eSkNvSQkTBUKGAgIiIi0lHSPNfU9mgaBiMiIn3VMn8e1S/OBtxgb8WTj1M353P3oPe73V9cghOJEG1qBNZek6BjwMAKDv0MtKH/DkVERET6KJG2GvBDzElqj2AF9OeTiEimW3rDdQAU7bgjTjhMzUuzE8fiv8fjKyVEamrd9rUEDHwdAgbxwolDmTIMRERERDqIV9LGHwAn1t4eDqdpRCIi0h/RpiZi4UhKW2JKQnylhJrqlPbuWDm5HfZzuuk5dChgICIiItJBcoZBYPjw9nZNSRARGVQidXU4odbURq9Yob9kGADhqioArGCwx2t1zDDIhlV0FDAQERER6SBR9DAQIDCslLxJk932WKyn00REZICt+NPdLL7mypS2RBFDIFpbQ7SpKeW4ZbnFbP0lxQBEqt2Aga+gsMfX6phR4C8q7t+gBxEFDEREREQ6cDoUPQyWl3sHFDAQEckkjR99QGjJYloXLki0ta1Zk9iO1NYSbW4PGORuOjGxHfAyDOL8BQU9vlZyhsGwffaldP/9+z3uwUIBAxEREZGOIvGAgVsYy7LcP5mUYSAikpnCFRWJ7bblyxLbkfo6Whe4wQTL76f8x8ckjvk7FC305ef3+BrJGQajf/YLfMGhX8NAZX5FREREkrStXEHFE38Hkv549HufsShgICKSmbzf07FwmKoX/pForn/7rcT2ttdfQ2jk+MR+oCQpYODz9XmVhGyggIGIiIhIksXXXgWOu5Siv6gIAMsrkOVEFTAQEclI0RjR5ibmn3Fqt10CxcWEkvZ9hYVuAcRYLPF7vifZsCpCR5qSICIiIpLEaWtLbMcDBvGK2o2ffEQs3NbVaSIiMsCSp4k5sSjhysoe+weHpU5BsHw+/MVe4UL/2j9Lz4ZVETpSwEBERESkG75Ct2J2vIZB1bNPU/nkE+kckoiIeOIFat3tKLHW1h56Q6Cw8yoI8ToGlr8Xj8YKGIiIiIhIXKKgVVKqamjpkjSNRkREUnhL4LrbMWLNzd12Ldh2+y6nHcTrGPRmyllgWCkAhdtt38eBDl79qmFgjLGAmcA2gAOcbNv25+tzYCIiIiKZIvmPzMCwYT30FBGRgeJE2gMGTizaKWDgLykhWl8PwLhTTuvyGvEMAyfUc3YCuEUPJ909CysY7O+QB53+ZhjsAUywbXsP4HzgivU3JBEREZEMkxQw8CtgICKSEZxI6pSEaHMTAIGy4RRO2TERDCjcfoduVzhIWSmhF3w5OViW1c8RDz79DRjUA2OMMQFgrLcvIiIiMiQlZxj4i4rTOBIREYmL1FS370RjRJvcgMGY409k/GlnJgoaRhsaur2Gv48Bg2zTr4CBbdtzgJeAD4ELgUvW56BEREREMkovltsSEZGB48RiLL/jtqT99ikJ8YK1ww+eDkDpAQd2ex0FDHrW3xoGI4FDgNuAnwH7A49117+srIBAIPsqSmaT8nJ92iKZQfeiZALdh4PbN95XX15e4mfZUpRH/HOsgvzgoPkZD5ZxytCne1HWt7aampTMgYK8AC2xMADlE8rJKy+mfJ9pbDRtZ/y5uYl+He9F/4TRrO7mmPQzYIAbJHjZtu0/G2MeB76gh4BBTU331Spl8CsvL6aiovs0H5GBontRMoHuw6HDystL/CwbKmoS7U2NrYPiZ6x7UTKF7kXpieM4OKFWfHn5vT4nFgqx6r5ZAOSMG0/biuU0NbTQWl0LQG2rgz/lnmsDur4XW5322gbZfJ92Fyzpb35dE1DgbRcAoX5eR0RERCQj5YwandiO1NW1H4itfektERHpnap/PMu8004mtGJ578957hkaP/0YaF+5puq5Z2j632dgWX0KPvhLlFXQk/4GDB4Dxhtj3gH+CZy+/oYkIiIikn5jTjwpsR2tbw8YOAoYiIisN9XPPwdA64L5vT6n5pWXEtuBsrKUY778gpRCtWsTX0lButavKQm2bbcAR67nsYiIiIiknZWbR87o0QSHD29vTP7jUwEDEZH1zldQ2GV7zb9epfHTTxh/+ln4cnOJNjamHA8mZYMB+AsL6AtfMEjRLruSO2Gjvg04S6jkr4iIiEiyaATLn1qseczxJ5K78SbujgIGIiLrRay1NbHthMNd9qn462O0zP2a2n//C4C2NatTjgdKS1P2ffl9CxgAjPvNqYw49Ad9Pi8bKGAgIiIiksSJRqFDwCA4fASjf/kr97jjpGNYIiJDihONMu+03yT2W76xWXbb71MyCJKngEVq3eKz4dWpAYNITU3KfscAgqwbBQxEREREPE40Co7TKcMAaJ8TqwwDEZF11rpoYcp+3Zv/pvnLL6j6xzOJtnBFRWI7HkiIZxgU7zbV/brLrinXKZqy0wYZb7bq77KKIiIiIkNOpM5dkitedTuFFzBwHAUMRETWVaSmusv2cGVlYrstaeWEeHt4zRoARv7wCMacOAPLstpPtiyKp07bAKPNXsowEBEREfFEqt0/YAPDR3Q+aCnDQERkfYnW13fZHmtpSWwnL7XYOu9bQkuX0vjxh+D3Exg+PBEsCJaPAmCjCy/BFwxuwFFnHwUMRERERDzh6ioAAskrJHjiUxK0rKKIyLqLNDR02R5tbk5sN3/1JQCjfnEcAIuvvhwnEoFoNGXq2MaXXM64084kf9LkDTfgLKWAgYiIiIgnnmEQLOscMEgsrRhT0UMRkXUVzzAY8YMfdmivS2xHamvxF5dQ+t3vMWyffbu9lr+4mKIpO26YgWY5BQxEREREPJF4hsGIzlMSLJ83T1YZBiIi6yza4AYMhu2zL2NnnExw9GivvYHGOf8DwAm3YeXmAFD+458mzrU07WDAKGAgIiIi4onUeEUPS8s6H7RU9FBEZH2JNjSAZeEvLKL4O7sx8fqbEseqZ78AgNMWxhd0Awa+YJDN77yHkUcezWa/uy0tY85GWiVBRERExBNt9P6ALSrqfFBTEkRE1otIfT0t336Dv7ikfcnaJPGVamJtIQLB9ili/vx8hh/y/QEbpyjDQERERCQh2tiIr7Cwyz9g41MSVPRQRGTdLLr0QqB9WkLcRhddCoC/pATHcXDCYXy5uQM+PmmngIGIiIiIJ9rcjL+gsOuD8SCCpiSIiKyT+NKJViA14d3vZRY4bWF3NQTHUb2CNNOUBBERERGPEwljdTUdAbAsLasoIrIuInW1rHrgvsR+/hYm5bgVcIMDTiRMrNUNKvjy8wdugNKJAgYiIiIiHicS7fSJV0KihoECBiIi/bFy1p9osecm9kf/8viU4z7v968TDhNrbnbb8gsGboDSiaYkiIiIiHicSLjbgIGlgIGIDAGxUIhFV15GzSsvDfhrt61elbIf7LCEbXz6gROJJKYt+AsUMEgnBQxEREREAMdxIBrF8vu77mBZ7f1ERAapttWraFu+jIrH/0a0qWlAXzueNQBQ1sVqB/GAbdOc/1H/7ttAe10DSQ8FDERERERwP9ECui+wpQwDERkCYklBgvlnnjqgdVmctrbEdvmRR3fukBSwrX39X+SMHcew735vAEYm3VHAQERERAQg6gUMuskwiE9JUNFDERnMok2NKfvhNasHfAwjjziqy3bLy+QC8BUUMO60M/Cr6GFaKWAgIiIiAjhhZRiIyNAXbUwNGMRrBWxo8VUPCrbdnuHTD11r/wnnXkDO6DEbeliyFlolQURERARwEhkG3RQ9tCy3joFqGIjIIBavW5C76URCixYSC4cH5HVjra0A+At6zhjY5OrrsYJBckaNGohhyVooYCAiIiICiT+au80wAPD5NCVBRAa1mJdhECgtJYS7hOGAvG6b9zs2J6fHfrnjxw/EcKSXNCVBREREBHBaQwD4cnO77WNZlqYkiMigFs8wCJSVAamFCDckp839HWsFew4YSGZRwEBEREQEiIXcdFlfXl73nXw+LasoIoNavOhhsGw4AKsffZiGDz/Y4K8b8wITvrVkGEhmUcBAREREhPb5tVYPGQZYPmUYiMig4kSjKYHOaGMj+Hz4S0rc/bo6Vs68e4MEQyuffYo1f/+rOw4vYLC2KQmSWRQwEBERkazXPPdrmr/8AgBfbvcZBpbPUg0DERk0nEiEb086gZV/+mOiLdbUhL+wkNzxE1L6tthz1+trx0Ihql94ntpXXyZSV0cs5E37yukhKCsZRwEDERERyXrLfn8TNa++DIC/oKD7jj5lGIjI4BFtbgag8eOP2tuaGvEVFpK76cSUvst+fxOx8PqrZ9Ayf15iO1xZQbShHgB/SfF6ew3Z8BQwEBEREUniKyrs9pilgIGIDCZJ0wxioRCO4xBtasJfWOQWce2g9tVX+vUyjZ99StXzz+FEo4m2lrlfJ7Yj1dVEamsBCAwr7ddrSHooYCAiIiJZzYlEUvb9hUXdd/b5cBwFDERkcEh+gJ93ximE16yGWAx/YdeB0bY1q/v8GuGqKlbc9QeqnnuGquefS7S3fPtNYjva0kzD++8BEBg2rM+vIemjgIGIiIhktXixw7iO83qTKcNARAYTJ5oUEI1GafJqtfiL3MDo8EMP63BC318jWl+X2G6Z921iO7R8eWI71tJC26qV7muXKGAwmChgICIiIlkt1tKS2N74iqvXuqxiuKKCxjmfDcDIRETWjRNOzaCKVFUC7ZlUIw4/gok335o4Hijt+3SBSENDYjvm1UxwIhFizU1u3RfcKQlx/mLVMBhMFDAQERGRrBbPMCjdd3/yNt6kx77xFRRW3HH7Bh+XiMg6i6YGDGpefgkAn1fc1bIsgsOHt3fw9f3xMDnDINrc5H5tcr8GR5YD0LZyBQDFu011M7Vk0NBPS0RERLJarNXNMPDl56+1r9YPF5HBJNYhwyDOCgRS9ssO+T4A/vweVonpRqSuPWAQqaxk6c03UP/uOwDkTXRXYmj2lmwMjhjR5+tLeilgICIiIlkt6k1J6HEqgscXDG7o4YjIBuDEYlS/OJu21avSPZSBFe1dwKDAGACcSLjvL5EUMABo+cam8qnHAQgMH0GwfBR4xRf9JSV9vr6klwIGIiIiktUSGQZ5vcgwCChgIDIYNX7yEZVPPc6iSy8itGJFuoczYDquAhPX8XdZfD8W7nvAIFJf3+0xf0EhuRM2at9XwGDQUcBAREREslqsxa1h4Mtfe4aBFQystY+IZJ5IdU1ie/EVl6RxJAMr6hUhBMifvEVi2wr4U/rFp2QlF4Ht9WvU14FlUbzrdzod8xUWkDNuXGI/UKyAwWCjgIGIiIhktT5lGGhKgsig5HRIzXecfqwfOAjFvOKDAMXf2S2x3THDIL7MYrSxgb5qXTAfX2EhgREjOx0LlAwjMKx9GUVfYWGfry/ppYCBiIiIZLX4Kgm9KnqoKQkig07t669R+dQTKW1OqDVNoxlY0aSAQXwpReicLRU/1rEewdq0Vaxxl1BsbKR0n30710bYcit8Re2vmztufJ+uL+mnvDoRERHJSuGqKprnfp1YN7w3RQ+VYSAyOIQrKvAXF+HLy2fNXx7tdDza0NirrKLBLtrU2L7jb5+G0DH46cvLI1BWRvPXX1H59JOMPOKoXl0/XFHhnl9QQHDESDa/8x6cUIjmuV9TtPMuWJZF4dbbkjN+AiW7T+sUUJDMp5+YiIiIZKU1f32Ups8+TexrSoLI0BBtbGThxecTGDmSUf93bNd9mhoJlpcP8MgGXqy5PcMgOavAl5vbqW/ZQdOp+Ntj1Lz2Sq8DBqFFCwEY8YMfutcNBiEYpHiXXRN9/EVFbHr1df0av6SfpiSIiIhI1mmrWJMSLIDeTUnw6dMxkYwX/1Q9UlnJijtu67pPY2OX7UNJ0xefU/efNwEo2nFnCrfZLnEsuQBiXOm++wGQu/Emvbq+E41S+fSTAPjyC9Z1uJKh9L+eiIiIZJ3Gjz7s1Na7VRKUYSCS6WKhULfH8jafROv8eYlU+qGs9vXXEttjf3MKlt/PJtf8FisQ6HJqgOXz4S8uJtrgFj5s+vILal55iXGnnN5lRkLz3K8T28mFDWVoUYaBiIiIZB9f5z+BelPQUAEDkczn9BAwGH3sLwEILVsyUMNJGycWS2xbXv2C3HHjyBk1qttz/MUliYDB8tt+T/OXX9D0+f+67Ns0x23Pn7wFBVtvs76GLRlGAQMRERHJOpVP/L1Tm2VZaz1PqySIZL74yiedWBZ+75PwbJiSYHURGF0bf3ExseYmnEj7MpTdFYRtW7kCgPFnntOv15LBQT9ZERERkV7yFbbP003+9K5t9Wrq//t+OoYkIkmcWIzl3dQt2PjSK93lAy0r8Sn6UBZrawPc+gW95S8uBlIDKk4k2mXfaGMjVm5ur1aYkcFLAQMRERHJKvFPxfqjZOq09p2kgMGiSy9k1b1/Ilw59OdFi2Sypi/mgON0eSxn/Hgsnw9fYSGhFctTPkUfipy2EFYgwLhTT+/1Of7iEiB1ysbqhx/osi5ELBTqsraBDC0KGIiIiEhWidTX9/tcXzCHgm23B9wK4R3F2sL9vraIrLtITU1ie+JNv2fiDb9L7McL/Vn+ALHGRpb89toBH99AirW2YuX07YE+4GUYLL/91kRbtKGByqee6Hz9UCu+XGUXDHVaJUFERESySrz4F0Bw9GjCq1f37fyAe35XAQOnrftiayKy4a155GEASvfdn+CIkQBM+uNM9+HZq1Ni+dyvoSWL0zPIARKtb8BfUtync+JTEjoKV6xJbEfq6oi1tuKEQvi66S9DhzIMREREJKskBwzKf/LTvp8fL+7VVYZBD9XZRWTgFO3UPm/fl5ubsuyfr6AwsR2prU1sRxsaaOtjADFTOZEI0cYGAiV9W+4wPiWho2hzc2IKx6LLLmLRpRcSa2nBUobBkKeAgYiIiGSV5PXHg2Uj+n6+F3BwYgoYiGQaf2kpvrw8Crbcqts+yUX6ql/6Z6I44OJrr2LRpRdS/967G3ycG1rEK+oYKC3t03ndZRi0zp/H/LNPJ1xRQaylpb1/UVH/BymDggIGIiIikl2SMgysnJx+nx+fkpBcOE1TEkTSx4lEiNbVkbvxJj32Sy7UV/vaqyy98XqiTU1EqqsAaF28cIOOcyBE6+oA8Pc5w6D7KQaxlhZq/vVKav8iTUkY6lTDQERERLJK8pQEX27fAwaWrz1gsPrPDxJtakocU4aBSPqEa6rBcRK1C7rTsVBfaMliIjXVif1IVXXHUwYVJxaj/v13AFKmYvRGoJspCXG1r72asq8Mg6FPGQYiIiKSXXztf/70J8MgXvSQaIy6/7xJ48cfJY45ChiIpE2kys0QCIwY3mM/K6/zygHxaQngBR4GserZLyQe7PuaYeArLEzZH/2rE9t3koKtiSYFDIY8BQxEREQkuyQVK/T1cckxoNOUhGTKMBBJn0i1+6AfGN5zbZLiXb/Tqa3+nbfbr+MFHgarlm+/SWz3tYaB5Ut9PCzaYUpiO2/TiZ36J2dYydCkgIGIiIhkldakpdSsQICNLryUiTf+roczUrVPSWivXRAvpKiAgUj6hL0aBMG1BAyKtp/CxJtvIW/zSYm2ujf/ndiONtQT9YoGDkbJNRpyRo9ep2tZwWBiu2PAwFdUxLBpe6zT9SXzKWAgIiIiWSXW3Jyynz95MsGR5b0+P1EDIRZLtOVNmgxoSoJIOsWLFgZHrH31k+DwEfh6mJJU/+F/19u4BpyvferA2rItulK8+9TEdvKqMnkTJyZtb8ak2+8iZ+y4fg5SBgsFDERERCSrxLMA/H0sBpbQxZQEf36Be22tkiCyVpHa2l5n40Q7BPh6Eo7XMOjlQ3Lyw3Cn1x3EGQaWlbTt6/vjXtn+B7Wf7/dTsNU25IwbR3DUmER78tKUMrRplQQRERHJKrHWVgDG/vo3/Trf8rt/gDvhcKLNV+AFDJRhINKjWGsLC847i7zNNmfjSy7vsW/zNzbLbr4BKxhks1tux19Q2GP/SFUVvqKilJT8nliBYKe2wu22p+nzOYl6CINRLGmp1/7I3WQTRh55NAXbbAvA+LPPBSBcWZnokzN+wjq9hgweyi3bd54AACAASURBVDAQERGRrOJ4WQC9fajoyPJ79QqSsgniAQMn1NblOSLZruHjj1h02cXMO+1kAFoXzCe0fFmP5zR+9CHgBufmn3Eq4ar2B9bQ0qXUvfUmAJG6WureeZtwddVa6xckS6x4kiS+KkD9O28N2oJ+sZYWAEYe/ZN+nW9ZFsMP+T55G2/i7vt8WD4f/qL2gE38mAx9yjAQERGRrBJrdR/0rdx+ptR6Kb5Oa3vAwJ+f715bUxJEOom2trLynrs6tTd//RW5PXxS3fGBPbRkCVZODnX/eZOqZ54CoGDb7Vlxx62Eli4FIG/TTXs9ro4ZBoVTdiRQUpLYr3ruaUb937G9vl6miDU3Y+XmMvygQ9brdX15+Ynt3I03Xq/XlsylgIGIiIhkDScSoWX+PAB8XazF3hvxooexUGuizZeXB5alKQkiXaj+4KMu29eW9t+2amXKfqwtxMq770pZNjBcsSYRLAA3gNBrHTIM/MXFqa/X2spgFGtpwZefv/aOfZRcDyFnzNj1fn3JTJqSICIiIlmj+sV/0rbMfbjw5axjwMBL+3Uv5scKBHDWce6wyFC06uVXUvaDY9zieeEeAgaO49C2alVKW4s9NyVYALDs5htS9gu22rrX4yr5zu4p+4FiN7ugaOddALD6OW0p3WItLYlCrOtb2YEHU3bAQT0WjJShRQEDERERyRpNX36R2O53lW8vYJBSvd3yAglJSy2KDGWxcJh5Z5zCmr880mO/1iWLqf/iS3LGjmPyrAeYfO+DbHrNb8Hvp+nz/xFLKh6azAm14oRSP+FvWbAA4P/Zu+8AOcryD+Dfmdl2u7fXWy69cYSEkJBQQuiKAoJIUbEhiIj8RAQBFQtIk2KhKDZQFBuiVCmKSO8QSgrJ5S717pLrZW/7Tvn9MTuzO7d7d7t7e23z/fzD9Hk32Qs3zzzv86DsQycMez8pizfr7iUHYOEddyXOLdYzDKrjc/+Tpx1NF5qmQQkFxyXDAACqP3U2qj/9mXG5Nk1NDBgQERHRPkFTVSg+n7me6xsyM8PAEjAQAFGytFokKkRqJIKuB/+B7gcfgBoMov/Z/414fHiH/pDvWXagXjxPEPTUdkWBFolg4IXn056nBPUMnqLF+8E5W58vrwb1mgauefMx+9vfzcvnkTyJQn5SiR4wMObqy/19ebnHRNJiMUBRxi1gQPseBgyIiIhon9D3n38j1tmhr0ip1dEzJZgZBomCbIIg6O0WGTCgAtf/7P/Q99QT6H/mvxkdH96xAwDgPWxN2v2BDe+n3W5M+XHUzzQLDxo1DwSHHa6Fi1KyhKrP/lxGYxqOFJ+SIHo8sNfWItS0Fdo0yxoyaqvknEFFNAQDBkRERLRPCKx/z1yed+0NOV9HENNkGEAAJGnaPVwQZSu5teFoNE1DcNNG2LzFKVX1y+MV/O3VNWnPNX6+xKIiCA5rNwPR4YQgiij7sHVqQtmHPpzx2NKxlZUB0AOArnnzocky5J6eMV1zomlRvbVrrjVaiIZiwICIiIj2CckPOoLdPsKRo0hX9BB6IEFTWPSQCps1UDa8WG8vonvaIPf1ouyggywV9gGg7NjjAQDaMK1I+5/TpzpIXi8Eu8Oyz/z5VTXrdkHIaGzDsZWVm8tGF4Box97hDp+S1IgeMBAcjlGOJMpMzuUtGxoalgO4C4ALwG8aGxvvyduoiIiIiPKo75mnLS3chvZfz4Y5JSGpR7zoLoIgSdDk9AXcJoISCKDtzttQcdLHULxi5aSNgwqXEgph8M3XRz0u1NyElptvNNfLVh6UcozRgSBdK1I1FrPcRxySYWA8DGvDFEzM1szLrkCktcVSz8AMGOzdC082rRonWXj7tskeAhWYnDIMGhoaHAAeAHBJY2PjIQwWEBER0VTWdf9fLetjyTAwHipi3V3mNltZOWJdnZD7+qBGIvCvfw87f/gDqOHQcJfJu1DTVoS3NWPPL+5AYOP6Cbsv7TuCH2xKu13TrG/6hz60lq1MDWAZc+yHZuoAwK6rv5c499jjUzIMJLfeMlDJ08+XZ+kyVMSnSBgc9TMBAMHNH+TlHhOl4w+/AwAEP9g4ypFEmcl1SsIxAN5rbGx8N5+DISIiIpoIYykIZq/Ve8gbc4XttXUoWryfud/3+mvYc+ftiLa2YMdV3x7bQLOgBPzmsv/ddybsvrTviHW0p9+RVOwz1teHaFenZbezsiLlFNHhgFTsTVsTIRY/Xyorg+h0pqTXG1MHihYu0q8/dx7mXvejzD9IBhz19RDdHkSH+8xTUHINlYqPnTqJI6FCkuuUhKUAIg0NDY8C8AL4TmNj45v5GxYRERFRfqixaMq2ofOps2ErK4PgcJgBg7rzL7BeT0v80q4M+qBp2pjnVmciuWXkwAvPI3DGxwFP6oMaUaZCTU0IbFqPyo+fDkEUEWlrA6C3Ogw1bTWP0+QYBJsNit+PHVdelvH17dXVCO/eBU1VzZ+h5Mwde0UlAEAckhFkBPxK1h4FR20dXAsXjelnOh1BECCVeKEGMqvZMBUY2RrupctQsvaoSR4NFYpcAwYeALUAPg5gDvTpCQcPd3B5uRs2W+7ti2jqq672TvYQiADwu0hTA7+HU0toz56UbWP9O9ozcyYC8XZxVTMq4K72wnh8Kin3Ivn9qr2lGeWrhv01KW8Go9b07PVXfgdr/vG3cb8vFa43Lr0Dst+P6qUN8DY0YOsbr0Gw2XDQ9Vej87kXsOfRfyHc3g5PZBDFs2vQ8d4bKdeQPPr0gXQ/cz11NQjv2I4ypwZHmb6/6f77zP0Vy5ea5zXFt62446fwJF+rZlWePm2qPV4vAt3d0+bf9LCi11Uprq1CTU3JJI9m6pouf59TRa4Bg24ATzc2NkYBNDc0NJSNdHBf3/SJzFH2qqu96OoanOxhEPG7SFMCv4dTT2DrLst6/dcuGfPfkVBRCcQDBv1BBYGk6w0Ohi3Hdm7aCnnO4jHdLxP+ji7LuhqN8rtIYyL79Wku2373B8jd+tQBTZbR65dhO2Qtwr+5GwCw9Te/w+xvXYXmX/zKPNdeXYP6//s6pNJSAEj7XVQ8+oNbR9NuuObpLxdDg4nAl3PtcSnnDYpuBCfoe63aHdBkGR17eiDap37XgXCr/m9AVHTwZ38Y/H/08IYLpOQaMHgWwC8bGhpuB1APYHo1KCUiIqJ9hjFH2rVwEer/7+uwxR9gxiK5x/nQfufakCkQWtL87vEkDwxMyH1o35A85UDp7zeXq878lLksuj1Qg4G0NUGK9muAc/bsEe9hK9enzMR6e+GaNx8AIPf3AQAW/erulKkIACAVFWXxKcZGjN9LDYYglk79gIHR8tIoCkmUDzlN9mlsbGwC8BiAVwH8E8Cl+RwUERERUb7IPfp7jaozzspLsAAAkDTVUnDpAYPiVasBAMqQPvXjFTBQQiG03fEzhLY16+sDAxCLizH723qF+bEUdqR9mzI4iJZb9CKCoseDRb+6Gwt//issvvteVJx0snnc3GuuAwBE9+6BpqoQ3XoHkeKVq1D96c+Meh+jRoHcp7c8jbS0INS4Rb/vkGBB+UkfQ8Wpp43xk2VHLNIfvNN1cpiKjFavxt8DUT7kmmGAxsbGnwP4eR7HQkRERJR38oD+dtSorJ4Pgs2WtKw/2LiXLoN/3dtQ4mnc9upqxLq6LBXk86n/2WcQ2LAewS2bseiXv4Xc3wdbZRWKFi+Ge8lSBDdvghqLpX1LSzQSIwgFAGogoBcATPNm31YRzxDo6kLTV74EQC+IWP+1r2d0H1u5/jMp9+oBg7Y7bxv22OozP5nZ4PPI+MxqaHpMr1aCesDAqBtBlA/5LSdKRERENMWYabqe/L11E6SkDIN4BwRB0oMIqhEwqJ0BYPwyDNSwXitBk2WowSDUcBj2Sv2NrVSsf1Y1qdUiUaZinR3mshEUSCdd94+Rjk85tlQvg6b4fNBk2cw0mCrEeGq/8bM21XX+6Y8AmGFA+cWAARERERU0Y4qAmMe5z0ZwwLItPk1B8esFtYyH9uTe6LlSIxEEN39gDT6oiWVlMH7PEn3KhVjsjW9nwICyE2lrw+C6twEAlaedjllXfmfE4+suuNCyLnmKM76X8TOphIIIfLDJ3D7n6mszvsZ4EuLZOWo0tTXrVKOEQoCmAcjvv3VEOU9JICIiIpoO1GAQgtNlyQoYq+QpCea2eBDBmJIgFesPTpocG/P9Ov9yH3yvvoLa885HyRFHouv+v8L/7jv6Tk3Dnl/fpd8z/qBg3NsIXhBlovOvf0b/s88A0IuEVpz0sbTf9WQlh61ByWFrsPWC8wBNg+DIvDigWVQwFMLgW3pLxtlXfR+uOXNz/AT5ZXyWoYVMp6Lkn3VB5Dthyh9+m4iIiKigqcFg3quGpw8YDMkwiL9pHXju2THfz/fqKwCASGsrOv74e/T/77+QexNNqqKtLfq9A9ZgRcef74MWf+tINBrf669BKvai/uJvYPa3vztqsMAi/j3L5hxBkiA4XVAGBhB49x3YKivhWrAw22GPG6P+hxYde9BvvKmBRJ0F18JFkzgSKjTMMCAiIqKCpgSDZnG1fEmbrWAGDKwP7fk0+NqrI2YNyD5f/N76lIRYRzti3V1wVNfkfSxUGNRIBC23/AiiywU1GEDR/ktQvGJl1tdxLzkAwc0fwFFfn9V5krsI0fa9AADvYYenrYswWYwMAzUWQ3jnDkilZbDn+d+SfDGChZWnnT6l/gxp+mPAgIiIiAqWpqpQQ0FIM2fm9bppMwzi24wWbMmFxzRZzu5tbRI1EjGXR5tiUP3JswFYgxVMT6Z0gls2Q/IUY9e1P7BszzW4NuOiryH4wQdme9FMyX195rLim1pTaAS7HjCI7t2Dzj/9AY4Z9Zh3/Y/Q/vu7IdgdqP3CFyd5hAlGxpGtPPOik0SZYMCAiIiICpYaiQCalv8iYGkyDIZmHYhOZ2IcoRAkrzenW0U72lPv5XRBi1grt1d/6mw444GR5ICBJo9PlwaankLbt2PPXXdAGRhIu99zwNKcriu5PfCuPmQsQ0PlJ04f0/n5ZkxJGHjxeQB64EBTVXOKUNWZn8zrdKdIy26Emrai7PgP53QuANhrmE1E+cWAARERERUsNd6XXMxjS0Vg5AwDk5hIC1bCuQcMkt/A6tcV4ZozB6GmrQD0ivLR1lZ4D19jHiJ5S8xlTZFzui8VpoEXn0sJFhQfvApFixZDCQbgPfTwSRoZ4Jw5a9LunY7RVlFL6pJgtGkFAMU3kNeAwa5rrwYAeFYcDHsW7Sk1RcHg229BLC5G0RSqAUGFgQEDIiIiKlj+eHu45F/y8yFdmv/QDAN7cmqwkvtb/qHTEIoPXmVprygVF6PkiLXWe1dUwD1vLoI7d0GTGTCghFhnZ8q2ui99GaJrclrxlX3oBPT/77+Tcu/RpLSIlCTIvkSwRfb54KibYTkk1tsLLRpJ2T6a5KlHWiy7IovBLZuh+HwoPfb4nKc+EQ2Hk9qIiIioYHU9cD8AILxj+7jfy2iraBDdbpQceRQAWB7ws2UUUfQctAKeA5ej9pxzISY9FAy9r6FsxUH6vTklgeI0WUakZTccdTNQeZqe/l//tUsmLVgAAK558ybt3qNJKVyqKNaaC4O+lHN2fOub2Pn9qzL6mVcjEXQ/+jCi7e2Q+xPXzTbIN/jGawCAksMmLzuEChdDUERERFSwPCtWIvDeu5h5yWX5vXC6ToVDMgwEm818EMvmAUDTNHTcdy/cDfuj5PAjoAzqGQYVJ30MRYsW6wclBQxEd/qHPTOowCkJFBf4YBPUUAjuNUeg4qSPofSoo2Erm9yq/2o4PPpBk0RMM93A/+475vLQIo2xnqRWp3va4Jw9Z9hra4qC5q9dqJ/X1YmyY49P7JMzzzBQo1H431kHW0Ul2ynSuGCGARERERUsxTcASBKcc+bm9bpamojB0FRgQbKZ0xSyyTBQ/IPwvfQi2u/5rbkOJFolAkBkt17gzDl3HsR4JfehIl3dAICexx/L+N5UuIJbG7HnztsAAN7D1kCw2SY9WAAkuopMRemmHg08/6y5HN6xzTKVILStKbE8SlZT90P/TLqPZGYSAdkFGAPr34MaDustKdkRhcYBv1VERERUsGK9vbCVl+f/F+k0GQZDaxgIdrsZRMjmAWDoW0vjQSI5PdponVZ2/IeGvU7v2+sAAMFNGzO+NxWmaGcnWm+9yVx3TaHCeKVHHQPnnLmYdfm3JnsoWfO9+gr2/uaX5nryvzPh7SMHDPr+81TSdV621CrJ5t8Lo04LpyPQeOGUBCIiIipImixDGRhIpPHn9eJqyibBNiRgIEmJIEKGGQaaLGPXNd8z10NNWyHH05yFpDaNdeeeh9DWrSg+5NBhryW5XFACgYzuS4Vt53cTD+N1518AQRBGOHpiSV4v5l597WQPI2eB9e8D0Fsu7v11IniQbd2U0NZGczmTgIGmqlAGByH39wMAHFOswwQVDmYYEBERUUEaeOUlQNNgy6I92VgIQ6cGSJJZ1yDTKQnRjg7LesstP9L7qwuCZcqDrawc3kMPG/HB74Cr9cCDvbY2o3tT4dE0DV3/+Lu5XnrMsShZs3aEMyhTtspKy3qoqcmyHt3TlrY+Q/9zzyKwcT3cS5ZatvtefcVczqRLQs+/HsX2y7+BUNNWCA7HlAoCUWFhwICIiIgKUuef/ggAKFq8X/4vnmZKguhymcsVHzsVQtJDfqYpxpG2lmHup2X9QOCZN1eve8AHibSCjVug5Lnd5lQx+M46dNx3LwbffMOS+l51+lmTOKrCUfvF8zD/ph8DAFwLFgAAtKSsI+8hhwKahvDuXZbzNFVF51/uQ9vtP4PsG7D8m5FstLoOmqah91+PmuuiwznC0URjw4ABERERFTSppDTv1/SuPgS2ykrUfeWr5rbk+cvGVIRsix62//bX1vscOrZ5yZLXa3ZZIJ0aDqH3qSfQ+uObse2S/8PeIX/mhaDj93dj4MUX0HHvPea22nPOS20TSKOq+cIXU7YJdjsEUYRU7IUa1B/ujayAGRddjOKDVwMAwtu3Wc7TkjqWRNtaYauqRvkJH025fvvvfouOeMAzneRsBABQQoUZ+KKpgQEDIiIiKjjJlcud4zC3V/J6seCWn6JkuAf6ePBAkLIvepjMvXRZTucZJK8XaiCQVZeGQjbw0gtovvgidD/4D3Pb4JuvQ1NTa1JMVz2PP2amwid/7xwzZkzWkKY1W0lJyjbBZgegtz+Mtu+FJstmwECw282sg6F1DDTZ+nNor66GvaYm7X0HXnhu2DENvPSCdQN/vmkcMWBAREREBSfa0Q4A8Cw/CI5JmMNvBAoEh17XQItGRjp8WO6GhjGNQ/LqrRiTW7btqzRNQ8cf7027Ty2QqQmaoqDnkYfS7nPMqJ/g0RQIIfVxyZhqZARm+p552gwYiHY7bBWVkLwlCO/YYT1xSODQUVUNwW5Pe+2RyL09GQ+faKwYMCAiIqKCE927F8DY39DnSpD0X7GMOcrpip8NZbwNLtp/ibnNVlYOAJC8qW85MyEV6wEDI4Cyr9I0LSU9vKhhf3O5f4S3udNJpGV32u2Cw8HpCDnyLDsQnhUrUXHqaea2obUHou174Xv9NQDx6QqCANf8+ZB7eyAP9JvHJU9JAACprAxAosZI7XnnW7JChst8MY5xLVyE2nPOw7wbb87twxFlgAEDIiIiKjjRvW0AAGf9zEm5v2q8bTQCBpHRMwxknw8AYEuuuSBJWHjnXZh/0605jcM1dx4AoPXWm+B77ZWRDy5g/rffQstNNwAAKk49Dfvd8wfMvvI7mHW53m4wunfPZA4vZ7HuLj0dXtOrcIaatqY9bvEvfzuRwyoogs2GmRd/A96DV5nbXAsXAQBsVVX6BkVFrFPvcGI8zDvnzAEARPckvltDpya5G/aHa/58iG43as/9EkrXHmXZny4zKdbbCyX+b8Xsb38XpUcfA0dt3Vg+ItGIGDAgIiKigmNkGEz4vO147QItHiDIJsNA8Q0AAKSkOdOCIEBye4atpj6a5JaK7b+7Oy9z9fufexbbv3255c3pVBdY/7657Jw1O7EcD6iMVpV+Kmr//T3Y8Z0r0fTVL6Pjj78HMKQtZ/y7KKWZg0/Zs9fUwjlnLmrPOQ9ifBrB3KuvAwBEOxN/7s45c/Xjq/TaBLHuLnOfUcOg9OhjsOiu38A1bz6cM2dh4R13ofTIowEAxatWm8crodR/Nzr/9mdzObnQKtF44beMiIiICo48MACI4rh0SBiJ6NTbm6nR7AMGcjxgkK7IWs7jGRJoyLWWQrLOv9wHuacHAy+9OOZrAfpD7rbLL0XTV7+M7mHm34+VUUXevWw5PMsONLeLLhcgCNOuhkGstxe+V182130vv4RoVydiXZ0AgLnX3ojqT30GAFCyZu2kjLHQiE4n5l59LUqPPsbcJrndAIDwtmZAEGCvqzO3GYGa5PohmhyLn2gz/60AYGmZWnf+V+BeshQAzL/PZFoG/5YQ5dPoVTWIiIiIpplwcxOAiX8DJzgcQCgELRoFkBwwGP0NtjIQzzAoLcWcH/wwoyDDaMQit/UewRBEV9HYrul2Qw0GEevqGv3gDPQ+/hiUeLZC7+OPoeoTZ+Tluskiu3ZBKi3DzG9cZnk4E0QRYlERQk1bsfXL52K/e/6Q93uPh/D25pRtA889i1hnJ8TiYjhnzoRjxgw46mrhPmBy6njsczQNoiMRBJA8HgCAEggkDolnGIxU2FB0OFCy5ggEN29CtK0N7v2shU+NeiaZFEckygdmGBAREVFBiaZ5KzdRjAcGo2aB6MyihsGAkWFQCtfceXAnFeXLlWNIy7ZMAhejsZWWAQB8r7xkzp0fC3VI1sPWC86DGg+4jOm6sRiCWzZDHuiHMuiDvaLCEiwwVHzsVHPZ+DuY6noe/1fKtr6n/41YV6f59yOIIjzLljNtfZxVJgW4kjN6JI9eZDI5w8DMPBrSGWEoR7z2SnintcuC4vdj8M3XAQBzvnfNGEZNlDn+C0JEREQFxUgvN3qhT6TqT50NACg7/sMAEg8QmaQRG3UX7DX5bQPpWbHSXE43V1+NRLD3nt8gFM/KGE1y5kNK27gstf38dvjXvQ2IIkqOiKfOa5pZz2Estn39IrT+5BZsv/xSaLIMW2VV2uPKT/iouRzrnLxgU6YiLbsRbW0BAMy99gbM+tZVZhE+ABCLxpZBQtlJztgRkjIMzJaqMT34pcaikHu6AQC2iooRr2nUXhkalNt26cXmstEylWi8MWBAREREBUWL6vOEixqWjHJk/hWvPBiL774XRfEHOMFmg2CzZTS9ILytGaLbDXt1dd7HZGi56YaUqQQDLzyHwddfQ9udt496rdD2bZD7es11NRgY4eiRKcEAAu+/F7+QCu9ha3K+1lCappkV6W3l5ag8/UzUfO7zaY8VRBE1n9X3xaZBf/tYX5+57Jw5C+79GjDzG980t4l2x2QMa5+VHKARXUkBA0kCAGiKAv+769B88UXwv/sOAMBWPnLAILm+wXAFOY2ABNF4Y8CAiIiICooaf6MnTtIv1EPT3gWXa9SAQbSzE7HuLrj3X5L3FPLStUeh+jOfM9f9xkM69IeZrgfuj6+M3EFh4KUX0PKj6y3bNFXJeVz9zz1rWfcsTcy1N+Z6A0CkpQWBTRuzurZZXA7AvB/dgsqPnQqbd/hikkZxTGVwMKv7TIa+p/+dsk1yu+E+QC+Up4whiEPZ8yxfbi4nB2uSAwaRlhZAUeB/Z51+XAZZICVHHAkAaLvzNoSamjC47m3L/sn69432PQwYEBERUUExCg4Ko8wTniiiywU1MnLAILh5EwCY1dHzTUuqCSCVJFKZ+57+j7k82lvP3n8/aS6bBdeU3AMGRmFK97IDUfP5cwAApcd9SB+vkuhXv+vaH6Dttp8guGVzxtdW4+3oHDPqM3rjnihQ5x/lyMnjX/8+tn75XITifw4LfnKbZb8Yr86fj+kclDmbt8Rsz5mcoWL+jMgylCFdODJpkyp59RoI4eYmtNxyIwIb9NagtvIK1H/tEhY9pAnDgAEREREVFC2mv12eKim7onPkDAO5vw+Db74BAHAfcMC4jKEoudK6msgkSG7bNtrDshpIPPSUrNXffmrxgEG0oz3rdP5IWyuksjLMuvRylB17PIDEQ5YxnUBLCki0/uSWtG/XU8YZDqP993cDAFzzM6tjIcYDBlO5veKeOxMBgsrTToetrNyyv+TwIwAAFSefMqHjIsA5azaAIW0QkzIMhk4rMIqhjnjNOfMs676XX4Jgs2Hej262TDMiGm8MTREREVFBMQIGo1UinyhifEqCpmlpq/Rvv+IyAICtsjLvBQ8NRQsXoerMT6L7wX9AUxIBg+QsDMXnG3aMgDXV3V6ld18wHuh3fu87AJBxW0IlGIDc2wv3UmvLPzONOx4wiHV2WPZ3PXA/uh/6JwSHAxUnnYKKk05OuXZg4wYEN27Qx5lhPYh0GQaaqkKLRhDcsgWeZQdO6hvdWLxYnqH06GNSjilesRLzb/kp7JWVEzUsiqv8xBkI79qJ6k9+2twmWAIGQzIMMpiSYK+uSdnmmDWbNSpowjFgQERERAXFqGGQrwyDZ3a/gOqiShxUnVs/e9HhBFQVmiynTJPwvfqKuVy0aPGwD+v5YLTbQ1LdAct4NE3PPog/6ACA3N+PwbfeQNmHTrBkJhjp0pqs5NQCsfvBfwIAnLNmWbabGQaKgtC2ZrTcdEPKuZosQ5NldD/4AMo+9OGUudzGw3Xlaaej/MTUgEI6RsBADQQQ2dOGwddfQ++Tj5v7a887H6Vrj8rw042u48/3IbytGXO+f435YDmSSIveFaHi5FNQesxxib/LIRgsmBz28nLM+6G1vodRiyTUuMWa4YPMOhzYystTtmUylYEo3xgwICIiooKSqGEw9oBBTJXxcPMTAIC7jr81p2sItvgDoaIAQwIG7ez05AAAIABJREFURuo8ALjmzc9tkJmS9AcYLenB33iTL3lLoAz6oKmK5QF2+xWXAgDEIrflUuYxigJlIPs589F2vYVk+UetD/RmwCAWQ/dD/0xsd7qgRcLwHr4Gg6+/ZrmOY0Y9oMhmezujdZ3noBUZZ5kIThcgSVD8fnT++T6EtjZa9su9vcOcmb3+Z5/BwPN6wUffKy+nzRZQYzEIkmQ+dBop7baqKgYFpqHQ1kYIDgfqvnSB/veaQWDQXl6O2nPPh2vuPHT+/a8Ibdk8LYpyUuFhwICIiIgKijklwTH2KQmBWCINP6pE4ZByCEIMmZefliCgLF7wb7wIYvwhPzlgEC8uKBh/VooKxBeT6y50/OF3AACxuBgzL7kMse6u+PlKTq0IFZ8PoscDW4m1c4Eg6X9Wit9vuf/cH1wDTVGgxWKWgEGsowM9jzyE0NZGLLjt5xDtdkT27AEA2Csyf7AWBAGCICC8Y3va/WokkvG10kme6tH5t7+Y24ONW1ICBnJ/P7ZfcSnKPnwCas7Wu1sYKe3SkMANTR9ikRve1YdkdU7pkXpWS9GChQht2YxoW+t4DI1oRCx6SERERAVFzWOGQSCWmHvc5t+b0zWMh+Dkyv+A9SG06qxPjf8c+fjb6uQaBkaXA9Gh931PbpNoZAEk8x5yGIoWLLTMz451d6ccl6zn8cfQef9fzXVNVRHr7U0p2gckag4E3n8P0dYWc7ujbgacM2fBNW8+6i/+BqrO+hQAYO9vfonA+vehhsOI7NoJ//r3ENqyGa6Fi8xChpkaGtCxVVVhztXXAgD6/vsfyP39GV8rsGkjBl58Ab43Xof/3XXYff0P0XLrTZY3xGJREQKbNlgKOwJAdK8e8Oh/5r/mNqPKfiZz32lqkty5B3uy/S4T5RMzDIiIiKigaLH8BQz80USGQZt/L+aXzs36GoJd/3Ur1NRkecMYatpqLk/Eg6CR3p5cw8B4SDZrGSQFE6LxN/XJjKyN5CCIkW0AIKVoYmDDevQ88hAAwLP8ILj3a0DbL+6EFgnDOWdOyvXd+y8BAAy++bq5zTnH+mdevGIloh316P7nA5bt4V070fefpwBJQu05546pHoT38DWoOftzEF0uFDXsj1DjFvQ//yyqPnHGiOdpmoZoayvabvtJ2v1dD/0D0DR4lh8EW3kFBl54DqFtzXAnz3EfMm41FoMcz+KQhmRk0PQxlp9x0enM40iIssMMAyIiIiooajR/UxL8SVMSwkpuaenGw/XeX99l2R5tbzeXJdcEvDmWUjMMjLfbRoHI5AyDyN7UgIFjxkz9+KQaBnJyBf8hb8sDmzaay+Ftzej65wMIblwPAHDFe9cnEz0eSGWJgn6lRx+L2d/5Xspx9qoqc9kobNj1t79A7u1FxUknwzlzVso5mar8+Ccw48sXQiouhmCzYcYFFwJIn3GRTI1FsfvG67Dr2h+k7oz/efleehEA4D5gGYpXrAQAtN56k/U6SVMxWm//GZovugADL74AALCVlub2oWhSzL/1Z+byWAIG3tWHwl5dg7qvfDUfwyLKCjMMiIiIqKAY873HWlFc1VR0hRIPw7KqjHD08JKnGqjRqFnVX+7vM7e7FizIcZRZjMOoYaClFj00WrUlBxOSx2coWXukfq34Zwjv3gXF5zP3K/5By1SDWEciKBLYtBHh5iZzPV3AQBAEOKprEOrvh62iArXnnJv+s0gSZl/1fQh2OyRvCfr+/SQAQCotQ8XHTk17zmgW3v4LxHp74JxtzXyQSssgOByIdXaOeH7/M/9FZOeOlO0la49CzWc/j+1XXKoXLxRFlBy+BoIzkQHTcsuP4FqwABUnnQI1mghMGcEVQM+0kLzMMJhO7BUVEGw2aLKcUjg0G1JxMebflFvRVaKxYsCAiIiICorc2wsIQto58tl4ZvcL+Nf2/5jrTX3bcOK847O+jtklAXpBOy0Wg72qCorfDwCYfdX3Ya+qHtNYMxpHUt0BTdPMZQCJh9fkoEg8mFC8chX8767Tj4unyxtvS/1vv2Vp/za0NkG0owNiURHUcNgSLACQ8mBu8CxfgVDTVhQfvHrEz1O0cJH+GeKfBQDKP/ThnPvUS8XFkIqLU7YLggB7dQ1inR1pz5MHBjD41hvoj3c+SGZpxxifElK0X4N5n+LVh8L/9psINW1FqGkrgh98gJIjre0bHbNmY8YFF44pa4ImUfzvXXRxWgFNTwwYEBERUUGR+3ohlZSOuYjgf3Y+Z1nf0tc0zJEjM6YkAEBw00Z0/uU+AIAnnpLuqK3LcYTZDkR/2I91daLpgvNQedrp0OR40cN4DQNNTTx8G/ucs2ebAQOD6Exkb8h9iUwEubcH2rz5EEQRmqzXN3AtWAi5rxdyT6KbQtUnPz1sBkj5R0+EvboanoNWZPixBLiXLkNw00ZzekK+2corEG1rhRoOme0bASC8Yzt233iduV588CpUnHwKQs1NKPvQCZY6CmpAn95iTEXQl1fA//ab5nqkZTe64l0Uaj5/DkLNTaj94pcybg9JU4/gcMRbveZeU4NoMjFgQERERAXDqMDvmpN9ccKhwkp49IMykBy4CDU1mstGJoQ4hurpWY0jnmFg1BXoefRhFMWLDArxLgnWgojxWhBpHuxTirBJEqAo6P33U2i/57eY871r9PNUFfbqagg2mxkw2O+eP4w8TlHMuv3crMuuyOr4bNnK9NoBcv8AHHWJgEHP449ZjvMeehhc8+bDNW/+sNdy1M0wl50zZ5vLxQevgv8dPTAjlZah7NjjUXZs9hktNLXYysoR9fuh+AYmeyhEOWHRQyIiIppwit+PjvvuRSzprXNerjvoAxQFtoqKMV0nqkTN5WWVS8Z0reSAgdEeD9Df9IseT6J7wXiL38csWAggtGUzBLvd7JJgKYgoG9MVUgMGQ1P33fvtDwCI7NwBTZbR//z/zLn4oqvIbJc4XdnihRiT6zoowQBC25otx3mWD58VUXHqaRAcDrgWLDS3OWbMgHvpMpQefQzcS5aa26vjbSNp+jPaKSa3USWaTphhQERERBOu/Xe/RWDDeqjRKGZ8+cK8XVfu7QUA2Coqx3SdrpAeyKh0VeCUBR/Bxp7NuV8sKSCQnJavhkKwT9R0BCRaJxqFDgHAvXQZKk4+xXyzbckwUPTjnLP0ufPJ7Q0Fmw0Lf/4r7PjWNyHY7Kj+9NnY9cNEd4CBF55HePs2APp0B6l8bPUkJputVB+/PDCAgVdeQv//ngFEEarfj6ozzoJ3zVoIomgWtEyn6rTTUXXa6ZZtgs1mZkcEtyayT4zMDyoAotGdJLeiqUSTjQEDIiIimlDdjzyEwAa9+rtZuX+MNFWFpsiQB/S037G2nwvE9EyAQ+sORr0n8VCvqAqkLMecPI9dCfgt+9IV2RsvRt0Bo6tBxamnmQ+wgffeBTA0w0CGYLOhaOEizPzmlSnTPKSiIiy885cQBMFSeNAQaWkBoAcqJqKo43gypiRE2/ei91+Pmtvt1dUoO+EjORdaTOasn5m47jQPsFCCmdGTY5cVosnGKQlEREQ0YTRVRW/SvG/R5UTf0//B7ptugBqLDXueGg7B/967aR9MNU1Dyy0/wo6rvgU5Pk9Y8nrHNM6Yqr9dd4h2SKJkTkuIqsOPcTjJY1aTpiQAgOTxjGGU2Rlai8ASADDeglpqGMjmdArPAUuH7SBg/Hf+rT+F97A1qcfY7XBM8wr/UjzDIDlYMO+Gm/RaDXkIFgB68Mi9dBnKT/hoXq5HU4PZnURmwICmJ2YYEBER0YTxvfaKZV0JBtH/wN8AAHJPt6UgXLK2O29HaGsjZl52BTxLl1n2BTdtQDg+lzzW3g4AkDxje3MfiwcGbPEOB3bJbm4vQvrq/sNSrW/tk0nFYwtsZGNowCC5zoNg1z+nlhS00WQZyKLThL2iEtWf+jQG33jNsl2w2+Gor0flJ86wTGuYTowaBoaKk08Z9rs6FuNdvJEmXtGixQisfx9Fi/eb7KEQ5YQBAyIiIpowRjq8IdLaai7LPl/ahzBNVRGKz+8ONTWmBgw2J+oLRDviAYOxZhgo+oOzXdQDBZKgv4FXckgrHmnushqNDrsv3wSHQ2+tGM94SJ62YbQKVEMhc5umyJaWkJndI7XXvCDZIAgCKk/5eC7DnhJsJSXmsuBwoOqMsyZxNDSdlH/kRDjqZ8J9wNLRDyaagjglgYiIiMZMjUahJb1JD23fjh1XXYm+p/+Dvb/7rflgLMTb8dWdfwGkkhJEW1vMc5RBazDBEN65M3Hd5kRVek1REOvtQayzM2l/E4D8ZRg4zICBnlasaOqw5wxnaFZBsmj73hxGlxtBECwdEgSb3VwWi+KV3JMDBjEZgi27eg1SURHKP3oiqj/9maT75KdOxWRK7nShjTB1hmgowWZD8YqVIxbEJJrKmGFAREREYxJpa0XLj29G8YEHoe78C6BpGrr+/lfEurrQFZ9uUDR/AcqO/zC0cBiAnopvq6i0ZBwog4Nprx+OBwEAQO7uNpe7H3kIfU89YTlWDQTi1889YNAR6MSj254CkJiKYAQMVC2HecgjZBiIztQ38uNKsgHxAEbyQ7BUZGQYJGosaIpsZh5ko/qTZwMAStYeCd/LL8F76OFjGfGUUfXJT6P7H39H8YqDJ3soREQThhkGRERElLNYVxdaf/YTqH6/WZ8gsP59s6aAof+F5wEkepELTifsSXPogeEDBrHeRCtC2TdgFhEMNW4BBAG28nI4Z88xj3EtWgxxDMUEr3vjJ/DH9MCDkWEgxosC5pRhMELAoPbz5+QwwtxZMwwSAQPRrQcGlOQMg6Sih7mQ3B6Uf+REsw/9dFfx0ZMw6/Jvoe78L0/2UIiIJgwzDIiIiChnHX/5E5SBfnO96f++Ai3NvPxoWytknw9y/FgjwyCZ7Es/JUGOBwycc+chsmsntEgEsUEfwju2w7VgIeZc9X1oioKmC88HANSec56lleFYeOx64MGckpBDDYPyE09GaPs2RHbuSNlnm+B2g5qclE6fFDwwpiT0PPwgPAcuh2vOXGiyYgkwEOBecsBkD4GIaEIxw4CIiIhyFuvuhOT1omTNWgAwgwWOGfUpVcHVcBiR1lYINhsctbWwDek1P3yGQS8Emw3OmXqfenlgAP633gQ0DWXHHAdAf3M+89JvYsZXLoKzvj5vn6/YDBgYGQbZBwzsFRWY+/1r0mY9TPQDeXIwJzmoIhYlph7svu4aRDs6oEXCepFEIiLaZzFgQERERDlTQ2GIRW54Dlph2T7rW9/BrCu/g9lXfR/2mloAgOIfRLStFY76mRAkKaXN33BFD1W/H5LXC3v8bfzO730b0S690KFz3jzzOM+y5fAeeli+PhoAoNhhzTC49e2fY8fA7pyuJRWlpuaPJeU/n8QhY2v58U0AgMiunZMwGiIimioYMCAiIqKcqeEwRJcL3tWHYO51N5rbbd4SCKKIooWLzEyCttt/Ci0Wg3PWLADWKv2CwwE1GEh/DzkGwe4wAwYAENm1S79PSWnac/LFJelFCY0MAwD40+YHcrpWrLsLAOA5cLm5TRCnxq9iUpG1uKHS3z/MkUREtC+ZGmFtIiIimnbCu3dBi4TNTAFn/UxUffLT5tQBQ6hxCwBADeoV+I0Hf8GeVKW/2AslGEQ6WiwGwe2BvTopYLB7F6TS0jEVN8yEkbYviompAzYxx2kEogioKipOPgWBDevzMby8ESa6WwMREU0LDBgQERFR1pRgELuvuwYA4Fq4yNxe8dGTUo412tEZxHjLQyHpwVt0uyH3dKecC8Sr9dvtKQUCPUsPzFtxw9EYUxKGLmdj1hXfRrStLaW2w1Qw3J+ja8HCCR4JERFNJVMjD46IiIimFbmv11yuOPmUEY/1rj7UuiHeZlBTZHOT5HZDDYWgqaltC7VYDILNBlupdfqBY0gmQz4M1wVBTSp2aExTyJZ7vwaUHXd8Tufm27wf3ZrRcfUXf2OcR0JERFMZAwZERESUNTUcBqC3DBw6/30o0eGwrBcfvAoAoMUSAQPRrRfdU0Mhy7Fd/7gfUFUIoghBFFHzhXPNfc7Zc3Ie/3C6Qz1pt5c6Sszlandl2mOyMeOrX0PtuV8a83VyZa8c/TOIbjdsJSWjHkdERIWLAQMiIiLKmhEwEDOY+y4kBQzEoiLYK6via5q5XYoHDGLx7geGvv/8GwAQatoKAChO6sbg3n9J9gMfRXuwy1xOziQ4pO5gnN1wBgAgmpQZkSvv6kNQeuTRY75Otow6EBPdzpGIiKYnBgyIiIgoa2okAgAprRHTEeyJbgjzrr/JXPauPhTuZcsx64pvmxkGu2+4Fv733gUAKIFE1wRbhf5GXCotRfmJJ6P+4m+MS4eBjmAiYHHUzDXmskOyY3nVUn1c2tgDBpNl3vU3YeHPf5XRsZqSOj2EiIj2LSx6SERERFnTzAyDDAIGooj6Sy6F6HDCVlZmbhddLsy69JsAgGC8kwIA9D71BIpXrITvlZfNbbMuu1y/liCg+qxP5eUzpNMR0DMMvnjA2Vhdu8Kyz2itqGrT90FasNkg2dL/+mevrUOso91cLzl8TdrjiIho38EMAyIiIsqK7PNBCektEDPJMACA4uUrRpxCYExJAIBYvFtCrDvxtt8xoz6XoWbNFx0EABxUvQyiYP01SYpnNCjTOGAwkjlXfR+u+QsgejyY8ZWLUPnx0yZ7SERENMmYYUBERESj0jQNyuAgBp5/Fj2PPWJuF1y5dQwYSkwKGCj9/VBjMbNOwkQKySFIggSHaE/ZJ8bbKU7nDIORSMXFmPO9qyd7GERENIUwYEBERESjCmx4H3vuvD1leyZTEjKRnGEAAM0XXWAuz/7uxD3EBuUwimwuCIKQsk8sgCkJRERE2eCUBCIiIhpVcOPGtNsz6ZKQCbHInXa7Z/lBcM2fn5d7ZCIkh+C2pW8TadQwUFRlwsZDREQ0mRgwICIiolEZdQVqz/2SZbujbkZerp9cC8E5e7a5XPP5L6Z92z9eQnIIRcMEDERBhAChYGsYEBERDTWmgEFDQ0NZQ0PD3oaGhmPzNB4iIiKaguTeHghOF0rWHmVum/3dqzMuejgaQZLMZe/hR5jL9oqKvFw/EzElhpgqo8g2/GcSBZFTEoiIaJ8x1hoGPwLQmI+BEBER0dQV6+mFvbLC8rbfVl6et+s7Zs5C8arVKD54NbyrVkMNBeE95PC8XT8TIUUvslhkT59hADBgQERE+5acAwYNDQ1HAwgC2Jm30RAREdGUowQDUIMB2BYsAADMuOhihLc1w1ZWlrd7CKKI+osuNterPnFm3q6dqVAsBABwj5BhIAkSFI01DIiIaN+QU8CgoaHBCeAHAE4H8Iu8joiIiIimlPD27QAA5yy9toB3lZ4FUGiCcjzDYJgaBoBe+JAZBkREtK/INcPgKgC/amxs9Dc0NIx6cHm5GzabNOpxNH1VV3snewhEAPhdpKmh0L6H/evfAQDMPHoNSgvssyVrk/XpFlWlpcP+HdokCYI4ff6Op8s4qfDxu0hTBb+L2ck1YHAWgK82NDT8EkApgI83NDR8trGx8d/pDu7rC+Y6PpoGqqu96OoanOxhEPG7SFNCIX0PNVVFz2OPoPfZ52ArL0ekambBfLZ02nt6AABaRBz2cwoQEZVj0+LPoZC+izS98btIUwW/i8MbNlCey8UaGxuXGcsNDQ1/APCHxsbG53O5FhEREU1NvldeQu/jjwEA7NU1EMTC7sacyZQEmyAhpsoTNSQiIqJJVdj/5yciIqKcRdrazOXQtuZJHMnECMl60cOR2iq67UUIxo8jIiIqdGNtq4jGxsZz8zAOIiIimkLUSAT9zzxtrlecfMokjmZihOIZBu4R2iq6bW5ElSheaXsDa2ceNlFDIyIimhTMMCAiIqIUwc0fmMsL77wLlR//xCSOZvx1h3rx313PAwDKnKXDHlcUDyb8tfHBUa+paRqa+rYhpsTyMkYiIqKJxoABERERpYh2tAMAPCtWQnJ7IAjCJI9ofP1968PQoAEAKlzlwx7nFB0ZX/PFttdw+7u/wRM7/jvm8REREU0GBgyIiIgmkaZpkz2EtCK7dwMAqj959iSPZGJ80NMIAKgcIVgAAHbJnvE1X9v7FgBg12Br7gMjIiKaRAwYEBERTZLep55E89cuRKipabKHkiLS2gLB6YK9unqyhzIhREH/leiSlV8Z8Ti7mHn5p9bBPQCAmcV1uQ+MiIhoEjFgQERENAna7rwN3Q8+AC0aRWDT+skejkVoWzOiba1wzppV8K0UASCqRKFqKpZU7IeqosoRj3VImU1J0DTNnOLgyGIaAxER0VRS+L8FEBERTTFqJILA+vfN9cD772PXtVdDHvRB0zSosckrkqcpClpuugEAINjG3ExpSmvq247uUC8a+/SWkR67e9Rz7GL6KQm/3/gX/Or935vrvuiguaxoyhhHSkRENDkK+zcBIiKiKSjS2mJdb9HrBQw8/xyk0lJ03vcHzP7u1ShasGDCxza47i1zWRn0Tfj9J8qLra/i71sfsWwrdZSMep5zmAyDdZ16AEjTNAiCgN5wn7lPURkwICKi6YkZBkRERBMssntX+h2CgK77/woA8L32ygSOKMG/7m19KA4Haj53zqSMYbypmpoSLACAclfZqOeumbF6xP0hOQQAuG/z381tzDAgIqLpigEDIiKiCaSpKjr/8icAgHPefMu+UHMztGgUACDaM6/Gny9qJGIGDBb94tdwN+w/4WOYCEYxwqEyCRi4bC4sKJ1nFkkEAFmVzeXBWAAA0BnsNrdt6N6c61CJiIgmFQMGREREEyjWk3iQrP+/r1v2BTdObvHD8M4dAABbRUVBFzvsDHWn3V7uLM3ofJtog6qpUDUVAOCPBwkAIBgLprTK7Iv05zhSIiKiycUaBkRERBNI7k48rEre4mGPU+OZBvmkhkPofepJSMXF8L3+GmZe+k3YvPq8/fDuXWZ2QcWJJ+f93lNJMBZMu73CVZ7R+TZBAqBnFjgkhyVjIRALmgEEj82NgJz+XkRERNMBAwZEREQTKNrZaS6L9kQBPc+ByxHYkMgw0KIRc1mNRNDz+GOo+OhJkIqHDzKMpu+/T6P3iX+Z69svuwQla9bCtWAhuh9+EGpQf9AVPbnfY6rTNA1hWf+z/fiCE+GLDuL5Vr1eRLHdk9E1bKL+65MRMNjlSxSxDMSC6I8MAAAWlc3H+92bAOh1E5KnMRAREU0H/D8XERHRBIp1dgAA3MsOBADYa2v19aXLLMep0URrxa5/PoC+p55A+7335HxfZXAQvU8+nrLd99or6PzLfWawAAAkz+jtBaejHQO78M0Xf4D/7n4eALCobAHmeGeZ+wVByOg6NlHPMIjFux/sHEwEDIJyyAwYzCyeYW6PKpPXKpOIiChXzDAgIiKaQLEuPcOg7rzzAQBzvncN1EAASsBvOS75AV7u69XP7U4/934kwa2NaL31JggOB7RYDMWrVptTD4bjnDMv6/tMB8+1vIyoEoUx2aPI5sqo0OFQRoaBosnQNA27fa3mvjfb38GaGYcAAKrdVVhetRTruzchokThsjnH/BmIiIgmEgMGREREEyjW1QnB6YRUohfYk9xuSG43xKSpBoLdjtC2ZmiKAkGSgCFF9DKlyTLafvZjfTleE8G1YCFqPncOlEEf9v7214i2tVrOKV65CraSkpzuN9XtGrR+1lwf4JNrGPSG++CPBVDrrkFHsBO7B1uhxYshljlL4bYXAQAiSgSAN/fBExERTQJOSSAiIpogmqYh2tkFe3VNSvq7VFRkLhevPgRaJIJIq57qLsRbLEbbWtHz+GPQVDWj+w2uexuaLFu2la49CraSEjhnzoKjptayr/7rl2LGRV/L+nNNB7IqozvUY9lWZHOh2JFZ3YJkiRoGCgaigwCA2d56c3+LXy+C6LG74ZT0oERUyV8RS03T8Oi2p/B2x3t5uyYREVE6DBgQERFNAE2W4XvpRWiRMBzVNWmPKTvho/Aecijc+x8AAOj44736DiURIOh55CEERmi/GGlpwa7rfwjfG6+j/e5fW/a5lyy1FE2s+fw5qPnCF+FeshSO+np4li4r2HaKL7S+mrLNKTnhsen1GryOzAs9Jhc9fDF+3bI0LRndtiI4Jb2wZSSPAYOgHMLTu57DvZv+mrLvmd0vYGP35rzdi4iI9m2ckkBERDQBWn96K0JNWwFJgnfNEWmPqfn0ZwAA0Xidg8juXZB9PkTjhRJdCxYivH0bAhs2oHj5irTX2HXtDwAgJVgAAHUXXGhZt5WWouyY41B2zHG5fahpQtM0PNScWvBRFERAAK4/4iq4pMynJ5gBA03GWx3vAoAZeEhWZHMlBQwiKftzEVNiuP71n6Td54sO4uHmJwAANx95Nao5BYKIiMaIAQMiIqJxpikKQs1NsNfVYdZlV8JeWTni8faqanN5769+gWhbK4pXrsKMCy/Ctsu+juDmTRnfu3jVatR+/otQQqGCrU0wmo5gp2X9kNqDccqCE8z1Cld5Vtczahgkdz6wS/aU45ySM+9TEp5reRmDsUSBTE3TzOkt9zc+bG7/zsvX4YYPXYlyVKdcg4iIKFMMGBAREY0jTVHQ/fCDgKbBOWvOqMECQG/v5z30MAy++YaelQCg5vNfgGCzwTlrNkLNTVBjUYh2x7DXsFVUouzY41B+0scgCAIk7777tnlDUor+Fau+hvmlc8d0PSmeYeCL1y8AALto/ZXqzMWnQhCEvE1JiCox3PzWHSnBD0VTYBNsGIj48H7XRsu+W176JW4+8pox3ZeIiPZtDBgQERGNI/+6t9H37ycBALayzFv42cqsb72NrgqO+nqEmrYi1t4B5+zZlmNiPYmifgtu/WmuQy44rfEihNetuQqVRdllE6RjE/UMg95wv7nNLlozDIwsBEeepiTsCexNCRYAQCAWwrqOdzGzWC+6WOYsRX9kAIB29sqfAAAgAElEQVQ1A4KIiCgXhVnZiIiIaIqIdXclVrJojyg4rXPqjbRze7xgYqynO+Uc/ztv68fU1WU7zILWF+6HAAFlzvxMyTBqGPRFEgEDSRBxzeHfMte7Q70AkLcMg45Al2VdFPRf4b77yvV4sPlxPL7jaQDAkfWHY1XNQQCA8qLUQoxERETZYMCAiIhoHEU7Osxlz4HLMz5PTAoYeA9fYy4rfn3+emDjhpRzAhv07gmzr7wq63EWsoGIDyWOYkjxzICxsgvxgEFyhoHkQI27CmcuOgUAsKRiPwAwaxiMNWDw6t43LetVRRWW9c6gHlDwOjz44gFnw2N3I6ZYW2oSERFli1MSiIiI8qj1oUfQ+vCjgCBAGdBTwyFJmH/TrbCVV4x8chJbaWL6QtVpZ5jL/nV6FsHA88+i9vPnWM6J9fRA8nphK+Wb5WS+mB81RVV5u56ZYZAUMDAyCY6fczRW1a5EqdNr2T6WKQntgU409+/Akor9cN7Sz6JlsA1vdbyLzmAiy8QfCwAAFpUtgCRKqCmqxm5/K1RNNbMRiIiIssX/gxAREeWJPNCPXX/8ExSfLxEsAGCvroa9otKcVpAJ5yy9PkFRw/6wVycq3Ved9UkAQOlxx0NTFMT6+sx9im/ArHVAuogSRVSJwusozts1jUyF5CkJla5EMMgIFgCJgMFYuiR0xLMH9q9YDI/djf0rFkNLM72l0lWOWrf+XSlxeqGoCoKxUM73JSIiYsCAiIgoT7Zffmna7cltEjPlnD0bc66+FrMuu8Ky3SiGKDpd6PzLfdhx5WUINTdB8fuhhkJZFVbcFwzGOxmUOPLXJcJjcwMAQnIYAPDZhjNR7U7f/SIfUxL8UX0aiteeCHqsrDkQc72z8fEFJ0KAHoia4akzg1LG503u5EBERJQtTkkgIiLKgqZpgKZBEK0x92DjlmHPcc6oz+lerjlp2v8ZWQqaioEXXwAAtNx8I6QSvaCfjRkGFjsHdgNAXjMMFpXNh0tyIazoAYNVtQcNe6wjD0UPtw3sBGANehxYdQAOrDoAgF4Q89FtT+GI+kPN/SXxz+uLDqIeLIJJRES5YYYBERFRFtruuA1NX/kSNFW1bG/98c0AgPnnn4fi1YkHt7oLLkTFqafl7f6SR38QDGzcaNmu+Hz6ftYvsHhlj14scFnlkrxd0y7ZsX/FInPdyCJIJ9cpCbsHW832iAMR/e92Udn8tMd+eM4x+MFhV+Cg6qXmNmYYEBFRPjDDgIiICIAmy4AopmQODBXcqHciUINBSMX6w3vyfPK6Ez8C2+FHY+fOHZB9A/CuOgSCLX//u7WV6pkE0bbWYfYzYGB4ue11bO3fhoWl87C4fEFer+2yuczlkWpT2EQbJEHKquhhSA7hlrfuhENy4DMNZ6B5YAdEQTSLLQ4lCiLqPDWWbQwYEBFRPjDDgIiI9nmaqmLbNy/Bnl/9YvhjFMUSGFAGfeayGtILyznnzYfocEAQBMy9/kYsuOWneQ0WAIDgdJndFmwVqfPmk7sr7Ov+1vgQAMCbx/oFhuEe3tNxSI6spiS82PoaAD0r4Y8f3A9ZlWETbVkVzSyJF158uPkJqJo6ytFERETpMWBARET7vEhrC9RgEIF330m7P9bbi+ZLvoY9d95mbpMHBxHYtBGDb78Fxa8XpXPOnGXuF+0OSN78P6gKgoDilSv1MfT2oOqMsyAlFTrklASdoirm8pmLT8n79e1ZBAycWQYMntr5v5Rt2U5pSK530B3qyepcIiIiAwMGRES0z2v/3d3msibLKftDWzZDi4QR2LDe3Kb4fGi77SfY++u74Hv1JQAwpyiMt+KDVwMA7DW1qDj5FNR+4VxzHzMMdK3+PQCAtfWHocJVnvfr24RsAgbOjKckRJUYYmrMXK/35FawMDmrQhKknK5BRETEgAEREe0TIi27EdyyOWW7Gg5Z6gE0ffXLiLS1WY4J7diWct7gm6+by72P/wvAxAUM3PsvwYwL/w8zv/FNAIDnwOUoP/FkeFashL06+xaOhai5fweA4QsFjlU2UxKckh3+WADheBvGkfhjfsv6OQd8GgAwvyRNx4wRJGdAKJoywpFERETDY9FDIiIqeGokgl3XXg0AWPzb31sKG0Y7OlKOH3z7TThnnm6uh7dvBwA4Z89GpKUFAOB/Z13KeUYHg4ngPSTRiUEQRVSf9akJu/d0MN4Bg2wYXRQuf/Fq3HX8rSMeu9PXYlmvKqrADUd8F5KYe5aArDJgQEREuWGGARERFbyBl140l5WA9Q1uYP37KceHd+zAruuuge/VVxBs3AK5txf22jrMveZ6zL32hmHvU7R4cf4GTTlTNRXb+negwlU+LtMRjHsAgIDRCxEarRUBjJpl8LuNfx5yrhPlrjJLTYJMnbz4OADMMCAiotwxw4BoHMgD/YAowuYtmeyhEBEA/7q3zOXonj0Q6gW033sPihYtRs+/HoWtvByStwSR3bsAJFontv8+UdvAXqO3rbOVWIsKzr3meuy69geo/MQZcMyoH++PQhnoCvUgIAdxQGXDuN1DjHcs0KCNcqTeJcHQGezGnJJZIxw99D65v9uxSfqveQwYpNcd6kVfuB9FNhdmeffNn11fdBAvtLyCDT2bcWjdwfjwnGMAAG+3v4t7P/gbBAi47dgbU4p8tvn3QhREzPDUTsawiWgCMWBAlGdqLIrtl18Ke20t5t94y2QPh2ifFuvpRnjHdoSam8xtrT++GcWrViOw/n09u0AQUPflC6GGQtjzizvgOXC5pbihQQ0GAQCS1wv3AUsR/GAT7LW1cM6ejf3u+cNEfSTKQHeoFwBQ664Zt3skBwFGY0xJAID2YOewAQNVUyEJEhRNwbkHfAahDGoejMQWn8bAKQkJmqbhyR3/xZM7n7Fsv2LVxZhfOmfCxvFQ0+No8+/FxSu+nFW7zHxqGWzDzW/dYa4/3PwEDqhowKPbnsTGni0A9IDYq3vexDGzjjCPe69zA+7e+CeUOkpw49rvTdr4iWhiMGBAlGfhHfq82VhHB7offhBVp585rvfTNA1yfz/s5eOTdks0ne349hXmsuB0Qovoler96942t5cefQzcDfsDABb85DZIpWXo+P098L32iuVa0c5ErYO6L12AnscfQ9kxx47j6ClXncEuAPr8//FidC+YVzL6Q2bylISOQCc2dm/G3JLZ8DqsNS/6wgNQNAWrag7CIXUrxzxGVdOzH3YM7JoStRwmg6zK6AsPoNpdCQB4ofXVlGABAPxk3S9wwbIvYEXNgXm9vy86iLAchqwqcEpOVBaV456Nf8a7nXpQ8oPeRiyt3D+v98xEm3+vJVhguPHNnwEAFpctQLHdg3e7NuCBrY/AFx1EvacOg1E/3unUp3ENRH1o7GvG/hWcikVUyBgwIMojTVXR++Tj5nrvE/8a14CBpmlou/2nCG7aiNLjjkfJYWtgq6iAvaJy3O5JNN7USASi0zn6gQCi7e1o+fFNmPP9H0IQRfhefRllxx0PwelCcNMGy7GlRx8LZWDA0t0AANxLlprLtjI98Fb7xfNSAgblHzkx6bgy1H7+nKw+F02c1kG9peLM4hnjdo9lVUtw4YFfxIKyeaMem9xRYVNvI/6961kASCmA2BHsBADUevKTGfHizjcAAI9sexInzD02L9ccbx3BLrT59+LgmuXmtvZAJ95oX4djZ61FqbMEMSWG3268D+XOUnx2/7NSrhGWw9jS14zG3ma82PYqAKC6qBJFNhd2D+odUM5cfCrKnWWo89Tghjd+CgC4e+Of8L1Dv4n64txaWcZUGR2BTtS4q3F/40NY3/0BQnLIcswJc441gwWA/l2d6IBBMBbCHe/8xly/5vAr0erfa9bPmFVcj2+svBCCIOCyF76PqBLFv3f+L+21/rX9PwwYEBU4BgyI8ii8YzuCGzcMu1+NRCDYbBCk3KtdR/fuQWDDekglJbCVlSO4aSOA/2fvLAOjuNY//MxqVrJxV0IECMEdSoFSpdSou/fW3Xtr1G791m57q/869VujlBYrrsEjxN1td7PJyvw/TDLJsgkSgnaeL+ycOXN2ZrPMzvmd9/290LR4EU2LFyFotaT85929jKKgcOTQVlpC1Sf/R8SlV+Bpc1Dyr2eIuOJqAqYct9djCx95AICCe+9En5BIW1Eh9QvmIyDgl5Tk1VcbEkrYeRf4CAaG5GSfcQWNhti776P0JWlCF37JZQQcP72vl6hwiCmxlqFVaYgwHtwSk8PC0vfeCW9jxHpHg/y6zd3uFX1Q2SEYRPZTKoVRa6C+tbFfxtodURT7LRR9V2MBX2R/R6DOQlaDlD40cPIjBOgtuDwuXlj/Og53G78XLea0ASdS0lLGjrpsAGYnneIVqSGKIl9mf8+6qk1e71HTWue1PSOu6/4yKWosKyskn5NtdTt9BAOP6KHB0UjIXiJWvsz6jtWV6/fYZ2HxEgBuGX4t7237hD9LlnF87GT8NPsmkh4oHtHDj/m/YXPZOT52MrOTTsKgMRBqCCHcEEqdo4HbR14v/22vGHwB7277hAlRY1hd0XVtF6Sezbyc7ylsLmZh0ZK9ClLryjbz2aYfGBaWzuykkw/mJSooKPQzimCgoNCPuOrrfdqKnnqC6JtuRaXTkXfHLfiPn4hh4EC0EZGY0ofu93sU/vMh+bXKz89nv+h04nE4etynoHCk0VZeRsOfC3Hk7aLoiX/K7VUfvY9l8pT9mpC0FRUC4LFKVRB2r35gGDgQQaUi/PIrcdbUoA0Kor2qSo4q2B3j4CGKN8FRiMvjotJWTax/9AGVIjxYtLna5Nct7S3oDV0RYVW2DsGgnyIM7j/uRm795dF+j7RYXLKcP4qXcvvIGwg3hh7weEtKV1Bpq6LS1pX243C3EQDkNOThcHd9Zr8WLPQ6Nr+piKa2JsptVZyTPKtHseDeMbfwwvo35O1zkk/32n/RoDlEm6P4JvdHqmw1uD1unB4Xfho96yo38dGOLwC4ZuilXpEPnZRbK/m9aLHP+4IU2j86YgTDQofw0AqpwsrIsAwGh6QyPW4K8wv/ZEvtdsZFjtrHT6tvOFwO/rXuNapba+W2GXHHYdAYAMlc8+7RN+Nwt2HUGuU+I8IzeG3as6hVas4aeBoPLH+SEL9gJkSNYV7O98DeI1iy6nN5PVNayCi3VTI2YiSrK9aT31TIZYMvkNNFFBQUjkwUwUBBoR+peOctn7a2wgJsWzLRRccA0LJmFS1rVgGQ+NRz6CL3LfTRbbWSd8ctXm0ehwNNUBB+A1Owrl8rt7fm5mDK8H2oUVA4knAUF1H85GO97nfW1qAL6z/TOn2ilMMdOHVav42pcORRYavCLbqJMx85rvfdqxS4ur1ubm9hY/UWQvyCGBk+jKz6XLQqDeGGA5+EA0SYw/DXmXG6nf0yXl1rA//L+5UNHTnsT6x+ntenP3dAlRz+Klslh+gH6QNpaJMiItpcbawoW8Pn2d8CcMWQC/m/HV/6HP/frf/nNRZAgiWO6zMuZ0vNDoaEpBFqCPZJ/+iOSlAxNWYi3+36mdWV6+UogavTL2Zp6Uq5X2b1Vh/BYEvNdt7pdg7Hx05iWGg6CZY4dCqtl2h1Yvw0/iheyhkDpfSmtKAU5hf+yZ/Fyw6KYFDUXEJzewsfbv8ct8ft9d0bEpzm4/Fh1pkwY/IZp/Ma/HVmHh53F0F+AejUWq8+9//1BOMiR3F28iwcrjb8NHr5e7G6YoNX37lrXpRfP7/+NZ6b8miv4p4oihS3lBLnH3NA3zMFBYW+owgGCgr9hOjxyK9DzjqHuh++k7erP/24x2NKXnyOpBde2adV1KYVf/XYbpk8hdCz5pBzbZdg0F5VhSa4lOZVK3Hk7SL61ttRGYyKk7HCEYV9+3afNtOIkeijY6j/9Wea/1pG6Dnn4iguQh8Tu0+pPDF33Yu7pZnKd6X8XPPoMQQcPx1tUJDy/f+b0GnkFusfc5jPpAu36OmxfUXZWnlyer1KQ62jnsnR49DuNhk7ECw6f+pafaPf+sK72z6mpMMDoJOchrw+57BX2Kr4MltapR4RNpTrMi7nx7zfWFC0iMLmEnkFG2BMxAjGRY5CFEU2124nyhTBk6tf8BkzOXAANw+/Bp1ax9TYift8LmqVGs9uf6cPtn8OSJ+h2+OmoLkYkML67R3eBN3FAoBTE2f6mFl2csbAUzgl8QQ5/WBgYCJ+aj8qbFU+6Sn7g0f0YHe2YtZ1TfY7Kxn0xo3Dr+rTe3VP13ho3J18lvUNRc0lWJ02FpX8xaKSrmeV0eHDyQgdwrqqjcRaorh52LU8uHyu13h2Vys763MYGjq4x/dbUrqCb3J/5LzUM5kWO7lP56ygoHBgKIKBgkI/4WroeiALOvFkHPl5PiHRneiionE1NeJubMRjs9GamwOCgHlE767Yts2Z8uvubu8Bk6cCkPjUszSvXkn9zz/hrK6k5Mfv5TJwebfdjHnkaKJuvBlBpSj0CkcG7RXlXtuJTz2L2hKAPWsnAE3Ll+FsqKdl1UqCTjyZsAsu2uuYaoPBS1iIuv7GA/IMUTh6aGxrks3rAAYFHTlGbHG9iBfd893/u1USlqfFTunX97bo/CmzVrChKpOmtmZmxE/t0zhuj9tHLABoc7chiiLzcn4g0hS+X5O6NR0rz+MiR3FRmmQQrFVJYkl3sQCQV5cFQWBEmJTOd1riTDZUb+bWEdfxyMpnALh88IX7VfKyO50RDoH6AKJNkRQ0F2PQ+HHHyBv4Ovd/bK3dSWNbE//d8jFFLSXyOWWEDuHs5FnoVNpexYLOa+juVaASVBwXM4GFxUu4a+kjaAQ1Fw86l/FRo/f5nB2uNp5d+wq1jnpOG3AiswaciMPVxte5P/r0PSPpFPRqPVqVpl9W62PMURwfM4mPm+f1uH9D9WY5GuWmcZdj8fhj1Biwu1qZEj2eeEssn2d9S72jZ58NURT5MW8+AH8ULWWAJZ4ES9wBn7eCgsL+oQgGCgr9gMfpxLpJyl0MnXMeKr2e4NPPRNBqvcq3dRJ+6eW0rFtL05JF2HZso/K/bwOQOPcZdFG+YbRuq5XW3Bz8BiYTc+sdoFaTd+uNAGjDJFMvXWQUgdNnUj//VxoX+boZWzdtoGnpYgKnn9Bv162gsL+ILheuxgbU/hasmzagDgwkZPZZ+A0YgC5SyrM2jxyF2mLB3dxMyyopHLhh0R/7JBgIej2C2G1bEQv+Nny682taXQ4ALko754jKix4fOQqLzp+3Nr+/x36Dg1P77NDfG50T2M7V8rGRo3qc1Fbba1AJap8wdbuzlZzGPL7J8Z6Adpretbnb2VCVKacD7KtgUGWrZmHxErQqDRenzZGjKqbEjOfnggVyv2mxk5kcPb7HMWYlncSspJMAmDvpQTyiSIih7yWGH5twL1vrdpIRMhitWku7ux21oEatUpNkSWRr7U5e2fg2tR0GiuHGMGJMkVyZflGfJ+BpQcmyEaJLdPO/vPn7LBgUNBXx4oY35e1fCxZi0hj5ftfPuEQ3KYFJJFri5fHHRY4iyC+wT+fZG+OjRrO5djtFzSU0tjX12i85JJGamhaenfJPRECr0pDTsAuQyjP2xG+Fi2j3SOk0DW2NPL/+de4efRMNjkbUKo0sHCkoKBxcFMFAQaEfqHz3bawbpZUSQ0c9d0NSEoYbJc8Bt92Gq7EJV10ttq2bMaSkSlEFQNVHH8jjVLz7DgmPPuEzvm3LZhBFzCNGojZLD3rRt92B2uida6gJCCDmltup/OBd3C0t6KJj0MfF0bJGcoW379ihCAYKh5XaH76j4bdfsUyagqe1leAZMwk8fppXH0EQsEycTMOC+XKbX6J3DXnR46H8Dd8a4iq9H9qQUHSRUViO69tKqsLRh0f0sLM+R96eEDXmMJ6NLypBRXpIGukhg9hel9Vrv+lxe68Msr90r9AAUNxS6lPGr9JWLeeVd/ck8Ige7v3L22dk1oATGRKSRq1dmjTXttaR21iwx3Oottfg8ri9xJBPdn4NwPjI0V4pGP46MzPjj+eP4qUcHzuZ81LP3KfrDPbru1DQiVat9fIo6B6pMCAgAUAWC+4efTNJHW0HwsDAAUQYwxgUnEJtaz3b67Kwttu80gt2p7m9hQ+3fU5OY57cFmYIoaa1jq9z/ye3nZd6JjHmKFKCkmhqa+53saCTa4deCsCti6WqNU9MfIAQvyBERFaUr2FwcJrct3uJUaNGMle0O71LTwJ8l/szf5YsAyDUECJ/7u9t/VQWGGbEHUeZtYJ/DLvS62/lET3YnHbMWhOrKtbxS8FC7htzKwF6S39etoLC3wZFMFBQOEDayspksUBlNOIX7/sAoTaaUBtN6KOjZTNCTZD0cCO2t3eNVVxE3S8/ETJrttfx9mzpAbO7kaF52Igez8eUMYwBz72IbXMmxsFDUBkMBJ18KiXPzMVZW3MAV6qgsG+4rVZad+UiaNSYhnZ9Z511tTT89isAzSuXA5LHQE8Ez5rtJRh4WqUHyrbyMgStFk9ra48pPyq9HpVeT+JTz/bb9Sgc+eQ3FQEQYQzn4XF39mt1hG0FddgdLoYPDEWvO7Bxr0q/iAZHExadP/cv7xKHT4ifSoQxjCHBqQd6uj5MiBrNmsou07m3Nn/AfWNu9Qrt7r7f7XGjUkuCQZm1wme80wacCEBLu1SN5JduVQs60wm6s74qkw+3f46AwN2jbyLMEMonO7+ioFn6m529W8UCgNlJJxNhDGd0xPD9utaDSYIlVn49Onx4v4gFADq1lkcn3AvAgsJFbK/L4sUNbxDkF0SkMZwL0s7y6p9dv4vXMv/r1fbCcU+gV+t4aMVTWJ02gvSBPDnpAVn42V0g6m863+eN6f9CROxKH0HguJjefSRMHdUYlpWt5KSEabKgsb0uSxYLAO4c9Q9UgooHl8/1ikbo9Eu4c+kj/GPYlWSEDgFgXs4PLC/zLp+bVZ+7X6keCgoKXSiCgYLCAVL02MMAhJx5NubRYxA0+/bfSh8XL782Dk7HNHIkNZ9/St3338qCQVtZGfYd23A1SnW7NcH7FmKr0uvxH9cVwukXn4DoctFWUozocu3zOSoo7C9uu538++9BbJNCwzXBwSQ+/S9UWi21333j078zDWF31EYjkVdfR9XHHyK6XLSXl+FsaKB47uOILhdh51/oe5AgoDL1viqncGzi9LjYUZcNwNnJp/WrWOB0eXh5niRMDRsYwh3nHdgE1qAxYDAbcHlccluMOcqnzF9/olX55vPnNubLgkFnScBOXKILLdLEv3vUxu70tKLv9Dhxe9zy38DtcfNT3m8AiIhe4fMAZw08zSunvxONSsOk6LF7u7RDik6t44ykU1hTuZFzUg7O32tMxEh+zP+NmtY6alrryGnYxaToscT5x+BwOXgj831ZaOnkpalP4qeRyijHmKPIbtiFWWs8LBUFBEHwiWjZE6Zu5RsXFC3mwrSzpdeFiwApsuC+Mbdi0hppc7d7HWvQGHCLbto72t/e8hGDg1O5MO0cH7EAYGvtDkUwUFDoI4r7mYJCP2GZNBl99L67cncXDLSRkQROmyFvuztWU2u+/JyaeV9g375Nmgz5+R3weXra2vbeSUGhj7RXlMtiAYCrvp6GBfNp+mupnBrTiXFoBipd7+ZklkmTSX7zHXm74N47EZ1OEEVq5n3h1TfhiadIfOo5pRLC34zfCv/kjiUPsaBoEQICyYED9n7QfpBd0iC/3pJXR4u9fQ+9953uYdlXpV/cL2P2RvdJWVdbl7C2e4qEy9NVeq8zwuCitHMACPHr8jeINkUyJWYCoX7BTIkez9COVexaR5cBcE1rHbWOeoaHDUUt+Ao5w8PS+3JJh42TE2fw6IR7CNQHHJTxe/JfmF/wBwA76nO8xIIHx97BoxPulcUCQP6MO/P+j3R0ah0XpEoRFHanZNLc0m6luKWUcGMoT0y8X/7+alXeCx0vTn2CV45/irERXeUod9bn8Niq5wAYGjIIP7WejNDBGDQGNtVspaDJW2z5u1NmrSCrPhdbx2ev4I3L4+LD7Z+zvipz752PcZRlRgWFA6DT5V0dEIg2ZP/qZguCgNrfgrulGZVGg6BSETjzJBr/+J3CRx4g7PwLse/sKjunMpn6pcJBwf13E/fQP/dL3FBQ6I4oijh27UITEgwI2LZuIWDq8YguF01LpZXKsIsuwbY5E/uO7V4lRrsTcfney3oJajURV11L1Yfv9dpHnzgAfUxsr/sVjk1cHheLS5ajV+uINceQEjgAg8bQL2Ov3l5JRZ0du0OKBBg2MIQteXVsza9j0tCeo2L6SoQxrF/H251wYyhpQclkdxjMAV4RDp1GdZGmCCptVV77OicS4yJHo1VpSQ0aKO8TBEEWEgD+LF7GtrosdjXmU9pSRrgxnApbJSDl1ydYYuXUEaPGwKiI4YQf5Gs/Grl40Bw+z/oWlaDCI3rYXLudf617TZ5QA/xj2JXE+vsaJI+JGMGO+mymxkw6lKd8QIyLHM28nB9oc7fR7nbyzpb/w+lxcVz0BK9+3SMmLhl0nvz6pIRprKva6DPuVekXy2LKivI1fJ71LX8WL+PajMsO0pUcXbg9bv698R1sLjsGjR8XDzqXdnf7Eef/cjhZWb6W9VWZrK/KZGBAIkatsc+lT492FMFAQaGPeNraKPznQ4B3tMD+EHPn3VR/9gmBMyWXZ7+ERADcTU1yHfmYO++hvaICQXuA/10FAUQRj8NB8dNPIrpcBJ92OuZhw/EbkLTXw12NDTQt/4u6H77DlDGMwJknYUpXHIr/bjjr6qj+7OMu/wC1Gtxuqj/5yKufPjoGy4RJlL/5Go78PESXNAnxHzeelg3rib3rXrTBwewLusieXeNVZjOGpIGEXXhJn69H4eihyl7DNzk/cnrSSSRY4thWuxOr08aMuOOYkzJ77wPsB//9aYfX9rSRMWzJq+PPDWX9LhgcitDx20ZeD8DG6q2w3jIAACAASURBVC28v+1TL1Ggs7KERWumkire3/YZdpedmfHTsDtb0ao06NTavYZzx3VMYD/P+tZnn0HjR3JgkiwYvDDV19xXQWJS1Dj0aj0RxnCeW/cqIBlVghSZ8sTE+3uNcBgXOYp4SyyRxvBDdr4Hiq7D8HJbXRYfbP+MguYixkSM2KMBaPfqCNHmSJ6Z/AgPrXgKkAxPx4SP8Iq8mBQ1js+zvmVTzVaeW/sqZyXPYlDwkVN29XCQ05CHzSWJUK0uB+9v+xSQIpI6vSD+7iwpXSG/fmTlM8T7x3D/2NsP4xkdPhTBQEGhj9R83VV32N3ceymhPeEXn0D8g490bScmencQBIxpg/plYh5z+52Uv/EaosuF2JGWUP/T/2iY/wspb/e+egvgam6m+Om5uBqkUFPb1i3Ytm4h/pHHfc9Z4ZjF1dRI0eOPyAaEALjdPv3UFgt+ycmotDri7nsQgNZduTSvWU3Y+RcQdf2N+/W+uoguwSDs/Iuo+UpKRwg96xyvVB6FY5dWVysvb3gLq9PGjvpsTkqYTptbuo91d7XvD0RR9GkLNEurSgUVPZd/O1rQdZgSNrU18/Sal5kZf7wcRdBZarEz7P2zLKmKQYDOf5/GjjH7rnh34qfxI9ESz+9FixkU9PeeqO0NQRAYEyGZGp818DR+KfgdZ4fAM2vAiXtMhxAEgShTxCE5z/6iu2C2tXYHAwMGcOng83tML7sw7WwEBIxa70iiAL2FV45/Go1K3aMA132sEms5r2e+y52jbuz3FKajibUdURljIkZ4hdx/kfUdGVMUwcDutFNl9zYKL24pw+Fq69F35VhH8TBQUOgjzppq+bXaf98eqPaGNsJ7JVUdENBvBoWmocNIefs9zCO9V4lElwu31brHY1tWr5LFAgDDoMEAtOZk98u5KRwdOAoK8LS2Ejhjppep5u4kvfAKKq132J4hOYWISy7zad8X1GYz2ohINMHBBHQrwagJPPAyagpHBqIo4hE9ve5fU7kRq9Mmb/9etJilpSsR6P8JUnmtzWv7zvOHYzb4uv8fKENC0ojzP7SpYZ1VDLbUbqfcVsnHO+exrW4nAC7RV/wDaGpv2aexTVojlw0+H81uXgUCAgMs8aQFJXPT8Ku5TgkJ32dOTJjGy8c/xSWDziPOHL3HigPHChekneXjV9DJcTETmRIzocd9OrV2v6J1Xtn4nx7FwWOdekcDVbZqilvK0Kt1nJ08y2t/U3sz2fW7ejn678M3uT/12H73sn8e4jM5MlAiDBQU+og2VPIs8EtKIuKKvedi7wuCSkX45VdS/fFHwMGZEEVe/w+cVZXoIqOo+/EH6n/9GUdhAaahGb0eY82UlOiAaTNwW62EnnMuhQ/dR81XX6BPTMSYmtbrsQrHDs56qQ6238CBWMZPJHjWGXKVkKATT8bV1ARqFYK6/1zqO0n452MgqFDpu5R9bfjRE3arsGe+2/Uzi0r+4unJD/e4glreYb43KCiFrIZcuT3REucVetwfZJc0em1HBhsJDegfb4Tu3Dz8mkM+YdGqpce+anut3DY8bChJAQn9Ygg3IWoMoyNGcMeShwjUB/D05Ie99h/s8n7HIipBxaTosUdc1Yj+5KohF/HhDilyLNrUcwragfLYhPtYXr4aW7ud1ZXrAai21xBh+nv8jrS6HGhUGl7a8JbsWzIwYACB+gBuHXEdvxb8QV5TAQBl1nLSgpMP5+kedNrc7VTba1haupKM0CE+BqzdS83ujkf0HJYqJIcTRTBQUOgjrkbpoTLm9rtR71bKzel2olFp+uTYbh4+gs7YBbG9/ysaqLRa9LFSOS19glRHuq20pFfBwLZtK6052RhS04i49HLpvLo95JY+/yxh519I0Emn9Pu5Khw5uFtaqPlcynHURUmhx/qYGOLufwhNcPB+m37uLyq/rglbwuNzaSstUYw7jxHqHQ1yPfUf837j8iEXAJIpl9PjIq+pgBXlawG4fMgFLCxawuLS5QAkBvTNP2ZP5JZ6p5iFWCRBIj0xiO2FDThdHrSa/nlYPNRVPcwd1RFEpHv4kxMflJ35K21VZNZs8zlmf00ZtSoNT016CEM/CzkKxy76jhDvKFPEQfs/EW4MlcuX+uvMLCxeQk5j/t9CMGh3t3PPskd92jt9RwYFpzAoOIW/ylbxZfb3aNX9H1F1JJFfX8wDS5+Vt9dUbuD16c/J291LeE6MGsuqinVexzc4Ggkx7JsH07HC30seUVDoJ0SPh/ayMgSdDpXRu2RVpa2aB5bP5cf83/o0tiYgkPBLpJBNbfjBzUXUx0jCQVtxcY/7a7//lrJXXwLAMnmK3C4IAlE33CRv13z1Je2VFdhzsvE4Wn3GUTi6qf3uG/LuvFXe7m7yaUhJPehiwe7oY+OwTDh6XMAVesbpcbGwaAnzC/6U2/KbCnF5XIiiyAsb3mDumhd5a/MH8n6Lzp9zUk4nxiwZD6YE7t2wdX+pbvC+h6lU0gRGr5PWWB55bzXFVfsWpn+kEWoI8doO8uuK5uhesaAzh35IcBp3jto/zxFp3MB+j/xQOHYZGjKYSwadx119+K71hU5Tv9rWukPyfocTj+jh3W2f9LgvxM87ijW4o2xqq9PRU/djAqfbyQMLn/Vq84ger5S4j7ZL0S4nxE2lpV1K2TV3K0Vb3lH95e+EIhgoKOwB0eOhraQYt807p7Vl9SqctTX4j5vgo4b/VbYKh9vB70WL+/y+AdNmEHnt9fKK/sFCGx6OoNPRsnY17m5Gdq6WZjwOBy3ru1RV4xBv40X/seOIufMeebv01Zcoff5Zyv79yt8yL/BYxeN00vDH7/K2X3LKIV8VVTg2+bVgIT/k/crKCil6ICkgkZrWOm5f8hD/y5tPSUuZHDrbiSAIqAQV9425lQfG3s6w0PSeht4nXG4P81cXUVVvp6GlK5qrrqnrXmjy6wrE9NNJqTY1jQ4+/DWrz+97ONk9jLb7dvfX4yJH8diEe7l+2BWyGaKCwsFCEAQmRY/FqDXuvXM/EG6URO7dTe2ORTJrtrGjrstvqnvEkAfvZ7WgjnSwCnvVoTm5w8C6qk3y63vH3MLg4FSgSzxqcDSypVYqaZ4UkMDYyJEAnJ96JtdnXAFAubV3waCgqYgPt3+Ow9X/EcKHEyUlQUGhF0RRJPeGa0AU0UZEMuBpKVzJbbdR8808BK2WkNO9S3l5RA+bqrcAkgFUXxEE4ZCsoAoqFab0DKybNmDfvg1ddDSV779LW1Eh2shIXPV1aIKCifrHTWiDfP0UTOlDib71DspffxVXrZQT25qbg33bVnTRMWhDQnyOUTh6cBQXUfzkYwCYRo7CMCAJ88hRh/msFI4ViptLvbYTLXHkNxUCsLB4yR6P1ag0B2QY6PZ4eOGLTeSWNvH1kjx0GhW3zMlAJQg0253EhZtJirYwc0ycfIxe1+XN0V8pCYeTBP+4XvcZNUaviAMFhWMJs9aESWOkylbdax9RFKmwVRFpCj+q89W7+5UADAtNl++v3VfNQUoJsej8WVu5kYvS5sglL48VWtqtzMv5AYBTEk8g0RLPkOBUdtbnkNdURJghlH9vekfunx4yCI1Kw6CgFMw6kywq7B5h4BE9bKzeQrBfIC9teAsAjaDhsiHnH6IrO/gogoGCQi/YNmdCx0q5s6oSj8NB88rl1P30P9wtLYScPQdtqPcDVVFzqewobXPaaWxrwuFyUGatJC0oGbPOhEf00NJuI0DfP5UVDpTAE2Zi3bSBirff9Gp3Vko3RL/EARgG9m5+Yxo2XH6tMhjwtLZS9u+XAQi//EoCp07r/5NWOOiIHg/Fcx+Xt8POvxBd2LGf66lwaGhwNFJp935Yj92tLN/g4FSC/QJl/4L+ZEN2jZdXQbvLw8vzNsvbaXGBXHxiqtcxftouwSA08OgPt7991A0+bTdkXMHG6i3EH+LqDQoKhxJBEIgwhVPQVITT4+qxKsOyslV8lfMDpw04kVkDTjwMZ9k/OFze6QXxllhenPoEG6u2MC7SewFAEAQijGE0t7fw0oY3eXDcHYfsPMusFRQ2FRNhCifOPwa9ev8rKvWG3WnHoDGwsz4Hl8fFhRlncFyYlGabHjKIb3f9zOaabST4x1LTIQo8PO4u2cvBrJOElWC/IHQqLRU27wiMn/IX+EQVV9l7F6OORhTBQEGhF1rWeT+k1s//hfpfpDIrusgogk8+1eeYP4qXem0/v+41mtutiIjE+8dw/9jbWVC4mJ8LFgAwJ2U2M+KO6/M5SuKDldUV6xkdMZx1lZl4RDczE6bJN1u3x01Nay0WncWndjFIgsCe0OwlSkAQBMIuuoTab74i/OJLqXz/XXlf9ccfYUhOUczpjkLqfvhOEszUahIem6uIBQr9htPj4oX1b9DU3uzVHmbs8sIwagxckHo2IYYgzk4+nXe3fsyIsN4ruewv3X0KkmMD2LWb0WFStGWPx7jcR2/aVUpgErmN+ehUvquHw8LSGRbW9zQPBYWjhUhjOPlNhWTX57KjPgen28mZyaeS25BPjb1WrqTwa8FCRoQNlX1Tjja6p3WdmngCI8KGohJUTI7puTTytLgp5DbmU2otP1SniCiKPL/uNbm0q0pQcdeomxjQD6a2P+b9xoKiRQwMSCSvI4JtXMwI6PA1jDCFE2WKYGd9DhkhUsnwC1LPItrsW61DJagI1AdQZq3ggb+eZHhYOuemnunz7J9giSPY79gq+6wIBgoKPdCydg0ta1YB4D9+Ii1rVsliAYBp+AgEjfd/n9KWcjJrtnq1da9fXdxSRku7lbVVXaVavs39iemxU/qUE97U1sxP+Qtk99buJosWvQWVIPD9rl9o7VCXQw0hPD7hPp/3Uvn5YR41GutG6bwSnngKR14eVR9/CEhVG/ZG4IyZBB4/HdRqL8EAoDU7SxEMjkLaSksAiL75VvTR0XvpraCw7+yoy5LFgkB9AI1tTQwJSZMNuHYvx2fQ+HHbyOv77f0r6mx8tywfgCeuHseG7GofwcDf6Lu6NTgxiA05Us6zy+Xx2X+0cPvIGxARFS8Shb81nSVG/7PlQ7mt009ldz7d+TX3j73tkJxXf1PvaADgzlE3khy45wUigBFhQwn2C6LB0Uibu71fV/p7o7ilVBYLQFoMe3HDG9w0/BrSQ6Sy3VW2ajQqzX5XJ1hdIQk/nWJBRuhgYgOiqKnpej4fETaU+YV/yiKRv673CGCT1gSttbQ4rSwvX0NuY4GXYeLZybMOaCHwSOXoTcpRUDiIOIqkWrThl11B8CldkQTaCElxNI8e43PM9rq9m2A9sPxJqu21hBu6VtKa2/ffbXtJyQoeWvGUT6mXTn4vWsznWd/KYgFIhi4lLWU99o+87gb8x40ncMZMdNEx+E+chOW4qVLeekpqj8d0RxAEBI1URjL2nvsxpKYR/8jjAFg3b0b0HL0P14cS65ZMHMUHXgu9r4geD6Io0pqfj23LZtRmf0wZw/d+oILCflDQJFVluS7jcp6c+AD3jbmVa4deRoDeIpsZHkz+t7xAfh1i0RNs8U0vCPLX+7RNGxHDE1ePA8DpcvvsP1roNI5UUPg7MyJs6F773DT8GkAKl/eIHpxup9fk8Ein3tFAQXMxcebofRILOhkelo6IyM66bBYWLeGJVc9jd9r3eExdawMvb3iL7Ppd+32eayqlBasbh11FkD5Qbu/0uWlpt/Lkmhdlf4B9paCpyCeS7fLBF/r064xey2+Snr/2ZPQ6K8k7PaUz9WB63BSGh6YzMiwDlaA65u6xSoSBgkIHrsYGUKtRm/1pWCCt1hsHp6MLDyf5zXdoKynGb2AyHqsVtb+3+mhz2mUVM94/ltlJJ/Pm5vd7fa/xUaNpc7fze9Fiqu01lFkryKrP5cyBp6JWqXs9ztpu46k1L9HitMptlww6lz+Kl1Jlr8GkMWJz2WVFGeD81LNYXraaclsl66o2EW+J9RlXpdURdX1XOSNBqyXyiqv38on1jHHQYIyDpLAuTWgo9m1bqHz3ba8yjH932kqKUfv7ownsClkTXS7KX3sVgLj7H8aQktLn8d1WK+VvvY551GiCZp60T8e4Ghspffl5tOER2DIlF2G/pCRlFVJhn2lpt+LyuAjy63rgq22t54usb8lu2MXU2EnMTjqZFeVrMGmNDApKQa1Sk2DpMt/r/vpQYNBrGNgt/eC1248jv7yZ6FCTT1+VSiA2zIQAbC9soKTaSly4UkFAQeFoJDUomUhjuI+XCkgpUeMjRzMoKJkxESNYX5XJwqIl/Fa0iMFBKVw/7IrDcMb7R5m1gmfWvgLA8XFT9tLbm05D1O7lGBcWL+WMpFN8nglqW+tYVLKcpaUrAHgt8788OuFer2oMe8LtcbOhajNmrYnBwancO+ZWyqzlvLn5fXbUZ3HqgBP4NleK8G1qb6bMWoG/zkyDo5Gs+lwEBE5KnO4z7sbqLby/7VPp+mMnsbR0JddnXN5jam6MOYpQQ4hsathdtNidQUEppAUlk93gLYycmXSq7HlwLKIIBgpHJLkN+by66W2Ghw3l+oyDU1rQnp2FNXMTrbk5qM3+tOZmI2i1RP/jZrmP2iQ9NKr0egzJ0gRud7EA4KucH6hurWVG3HHMSZEqJ2hVWpweJ/5aMw+Pv4tN1VuZl/M9AImWeBo68sq21O5gUclfgHTTGh81usfzdbjauH/5Ez7tk6LHMSl6HE63E7fo4e5l/wSkslhzUmZj1pqYFDWWu5b9k8LmEvk4t8e9R3GiP9BHReOqraVl3Voir7kee3YWKoMRQ1L/104/WnC3tlL0xKMIOh0pb/23q93aFWlS8q+nSXn7PZ+0l32lec0qWnOyac3Jxn/MODSBvf/4dVL5/ru0l5fTXt6Vt9jd0FJBoTdEUWRd1SbmZX+Pwy2Vknpk/N1k1+/i69z/yf2Wlq6QHypPTTwBP43vKv7BprXNxdqd0gThsSvHIggC4UHSA6TFpMNs0DJsYO++LYIgoNWoaHd5eOyDtXzwwIxDct4KCgr9zz8n3EO728mCokX8Vvin3H7P6JuJMEm+PZFG6d/OtM/Ntdtxe9ysqlhHibWcC1LPOiJXk7fU7JBfjwoftl/H9rSw9HvRYtZUrOfUATOZEt1VUvzP4mUsK1vl1Xd9VSYnJUiT+J4MJTvp/BytThvHx05CrVIToPfHqJGeEfObilhTscGrFOIza19Bp9LS7nHKbVHmCIqaS2hqa+HiQXOostfwwbbP5P1zkmdz+oCTMWh6NqsVBIERYUNlL4Igv4Bez1kQBC4bfD6PrHxGbhsbMfKYFgtAEQwUjkBcHhevbnobgM012/g2+0dGRAxjYGBiv71H86oVPrn2AGJbG6Uv/gsAfVycLBjsCYerjc0124gwhnF28iy53eVxATAxeiz+OjNTYyeytXYHO+qzSbDEEtQuTeI6xQKA1ZUbehUMdtRne20/OPYOr5ufVq1Fi+TsWtNay/Bu4XZatVY2+Jm7+kX0Gj1FHeLBpYPOY2L02L1eJ0gTg5Xla7E57UyLm7LXkjthF16CbatUZjL3H9fK7anvfbRP73e0Y92SSdPSJURdfyMqvTQ5cuRJqrTY3o6juAi/+AQAWtZ6507m/uNaom66Ff9RXd8Hd2sraoOvOi7vt1ppr67Gur4rVaVx6WJCzzx7j+fptlqx79zu1aYJCibgeF/VXkFhd/6XN9+nDOJTa14CpIdFt+jxCeMdH+mb1nUo+HlVofw6IVISf7UaNc/eMAGjXnkkUlA4FHhEkaf+bz2FlS3MvXY8MT1E9BwqdGots5NOJtQvmOyGXVwy6FyvyV+ncNCdb3f9LIufBrUfM+KPw7KHvPfDQZVd8lu5Z/Qt++1DEGboWTRtam/hy+zvMWlNmLVGshvyKLNW+PT7tWAhi0uW0+pq5fqMy/ku92fCjWHcOPwqVIIKURQpbC7m06xvqOyoODA2oqtig1atRSWo8IgePt45D4DJ0ePJrNmKzWnHg7fp7NtbPpJfd/ehmBF3HJOjx6NWqTGqen92AiktoVMw2JsAFOQXyNXpFxNlisSkNWLU7HnsYwHl11GhX9lauwOjxtjnyX1+UxFfZH0rb4c2uMj4/Dusuu9Ze9c1jEvev7Cq3uhJLNiduIceBaRJMuAThuVwOfirbDUGjR9Oj4tR4cO9bjJixw3Nv1ud25tHXINH9KASVPip/eQQKLPWRIghmJyGXeysz2FwsK9vQGdIFsBxMROJ9e/ZiC7aHNmju2uIIYhyW6VP+N2nWV+THjpon37sCpqL+Txb+vvkNuVzc0d+X2/oIiJIevEV8u+5c69jH2tYN22g/M3XAeQoluKnHvfqU/zkYyS/8TYqPz+c9VIonDYsHGeN9DeqeOct/N95H0dhISXPP4PY3k7sfQ9iTE3zGkcURezbt0nlLDu+r9qwMNwtLTQtXUzI6WcgqHuPJul8P+PQYdi3SQJP8OlnKOkICr3SGaG0tXYHf5YsI0Bn4a7RNyIg8Oiq5+R+D469gwhTOJ/t/EZ+kHtuyqN7zBE9WDhdHuavlvwTpg73djyPCDLu8zge8eitkKCgcCSQXdxIYaUUVffN4l3cft7hj2abGD22x8WTlEDfiMhOsQBgYfESsupzeKCjBKEoijS2NWHR+R/0KM49UW2vQSOo+1QiVSWoSA1KJqdhFwmWOAZY4lnS7Zq/zPoOm6vL0yDMEMJ1GZdj1pp5eu1L2Jx2Wl1SVZn/bv0YgFpHPTvrc0gPGcSmmq1yukAnibulo70+/TnWV2VSY69Do1IzIWoMZySdQpm1ghj/KP5v+5eMjxrNrwULZXGkO3q1jlkDTsSvl6iC3UmwxDIoKIW4ffy8Rkfs3RD8WEIRDBT6jeLmUt7e8hFmrYl/HffYHvtW2KoIM4Sg2S1U6avs7ym3VRJhDGdWlhrtGmnyYmgXMTz3HpVTcoi49PI+h2oDNC1fJr8OPv0MXA0NNK+QVvmN6UOxb9+GOiAQlVaLKIq8veVD7C4Hd4/uysG3ttt4dt2rXuVqdg/5umX4tfxZsoxJ0eO82jtFBUEQCNRbqG2tQy2oGByUQlFzCW9kvsebM573Oqa4uZTGtibUgpqXj5/r87ntC+VW77qxEcZw2awltyGf0RF7/sHOadjFvzd1hdDvqMumwdHola/cE5rAIMIvuYzqz7py4UTx2Hfo7hQLAGzbt9G4cEGP/ZpW/EXQCSfiKCwAQSDiqmsoff5ZaadbMlZrXr0CsV2qAdT011IvwcCetZOyV1+S/k+IIv7jJ+I3IAlTRgb1v/xE88oVtOZkYxw8xOe93XYbHpud9g7BwJSRgf+o0bhtVizjJ/TL56Bw7FHQVMxrmf8lzhwtO09fMvg8Qg0hiKLI1JiJNLQ1cWHa2QTqpdDO81LPYFBwCiPDMw5b+O5H83cCktHhlacO7vM4ASYddc1tPu2lNVYQIVbxNVBQ6JWyGisvfNEVYr45r46rn1vEzWdnMDpt3/LeDyX+OjOhfsHUOuo5O3kW3+/6xadPibWcekcDze0tvLD+Dbn9+eMex6TddzGyvxBFkSp7DaHG0D6LFrePvJ7SlnKC/YIwag3UOxqptFUxMnwYC4oWefWNNUfLZSdPjJ/GD3m/eu1PCkggv6mIDVWbSQ8ZRGa1VFEsxC+YAL0/Vwy5qMdnwjE9TMrTgpMBaQEOpJSRD7Z/RnJgEiclTGd9VSbJgQNICkjYr98alaDi1pHX7XP/vxt9mnWlpaXFAq8D4YAHuCQ7O7u4P09M4fDSmp9HyTNzUfv7E3XjLTQtXYLGYiFwxky0YT3f0H/ruIFYnTZ5Fb0nttbu4O0tHzEz/ng5hL/V5SCnYRel1goGWOK5yTSD0uXP+xzbvHwZuvBwgk87HVEUKfnXMzh25ZL47PP7VCfebbNR/YWU1xRz172YhqQjejyoLRb8x45DFxFJ7bdfEzjzJNweN8+se1UOl7I6bZi1JpraWngt879eYkGUKYIoU4TXew0OSWVwyJ4rDLS5pUlggiWeGfFT5c+w3d2OrlsI2cZqSTi5LuOyPokFAHH+0dQ56okwhnHN0EuJMUexq7GAVzb+h4Kmoj0KBjvrc3gj8z15+9TEmcwv/IMPtn/GbSNv2GOOGkDg9BMwpKRS9Ljkr9BWUiyH4h9LiB4PbqsVjcWCymjEY5cU+N3FAkGnI/rm2yh75UVqvvgMTWAQjl25GNIGYUxNI/7hR6n84D3aK8ppr6yg8Y+F0nF6P1pWryJw2gxElwtHQT72nTsQXS5El5QCE37hxbLPhv/4iTSvXEHpS88T//CjqC0WtCFdFToq3n4L+47tGNOl9BVdeASmjP3LdVT4e9HqauXFDdIDcadYMCZihFz6ShAELkjzTYHRqXV7FSUPJm3tblZtl+7lp4w/sHvPgCgLdc3SipbT5eaH5QUMTQzmhS8zARRfA4W/DR6PyEvzMtFr1dx27t5/O75evIv5a6TpwswxsQSYdHy7VCpxumZn1REpGAA8MO52yq1VJFriqLJVE22OYkrMBLbX7mR+4Z+UWsv558pnfY77z+YPuW3kdV7Pc4cCq9OGw+0g3DDwgMbpHsl6XcZlshDRXTBIsMQxNXaivH1iwjSmxU1Bq9Lweda3bKjK5Or0S3hr8wesqdzAtNjJZNXnEqCz8MTE+w948SjWP5pHJ9wrb5+SuG/336LKFvIrmhk6IBiLSYdeu2dhZdHGUhIjLSR1M8n9O9HXZVob8K/s7OzVaWlpNwD3AEdngVKFHql8X1pJdre0dK12Ag0LFxB63gUEn3wqottN+X/eoKylCf/TZrO5bpvcr6SlrEe3a6fHJeca/VG8lBhzFAICH+34Qu5zRnsypW97iwWZI0IYkSmFbDcu/pOGPxcSfOosHLtypfd7ei4DX+1a0XVbrdT9+D26qGj8J0yi8Y/faV61EgQBsa2NoFNOwzQkHQBBpSJsznnyseEXX4rdaeeBrTPx2QAAIABJREFUvx6TJ/QA1fZaPi36mq21kpFMd5fUCGNYn256lww6l8+yvmFOymxMWiPHxUzkr7JV/Fa4iDMGnoLT46KutZ4KWyUAAwL6/qB7yaDzSA8ZzISo0bKYE+8fi0pQsaF6M2cnz+pViV5ethoAs9bEoOAUMkIHM7/wD/KbirhjyUNclHYOU2L2vCKtj40j4qprqfrwPYqffIykl/6N2t+f2m+/wpA6CPPwoz+8q+zfL2PfsZ2YO+5GdLnQxcYRes65lL/2itxHExxMwmNzvfwxKv4jTcDMIyW/Ar8BSfiPn0DdD99R+MiDcr/Ia66j4q3XqZn3Oa7GJlwdaQyd+CUleZlymtK7fCyKn34SdWAgSc+/jKBS0V5ZgX2H5Ftg374NXUwshkF9X3VVOPapstfw743v+LRPi+2fVLGDyYK10iTl1AnxnDDa19Brfxg3OIL12ZJgsHp7FfNXF8upDgDWVidmw7FtgKWg0NrmorCimZ1FUlWmvUUOLtlQIosFsWFmLpiRjIBAoFnP+7/spKHF0eNxoijSbHcSYDq0k+7uGDQGOdX2ksFdz4sjwjPICB3CQyuewuq0ye1XDbmI34uXUNBcxHvbPuW6oZf1aoq3rHQlayo3MjvpZAYF9706Une+yP4OgOC9RIDuDypBBQJei2NTYyb2KBB3LiJdkHoWZyefhkFjYErMBL7K+YF/rX9NPvZQR5p6RJFfVxXx3bJ8r/apw6N8os48osjijWUY9GpSYgP59Pcc4O8rCPdJMMjOzm4AVndslgOT++2MFA47oijittl63V/79TwMySkIKpVcfq3uy48IGqdmUKF0w89J2tWjYPBrwUI0LhFDmwerQcXH277AT9dlFjJWnQAffAlA8OwzqRdaaWusp3lkAG+kbeGWeTW4GqQfp5ovP5ePc1tbcDU24Kyvl6IEvvuapmWSeYmzvp7GRX8gtnWFkJpHjNzjZ/DB9s+9xAKA1za9g7PDyHBwcCo3DruK25ZIk7kI496jG3oizj/Gq+Z4Z67ZgqJFJAUksLx8jSxQaFVaTJq+h7YZtQYm7Zafp1NrGR42lE3VW8hvKiIlKIl2t9PLzLCwuZjMmm3EmKN4cOwdCIIgGzp28kX2d6QGJQMiwX5BvUZB+I8dR9WHUqRCa042arOZhgW/0bDgN5LffEc2BjzSEUWR6s8/QaXTEXruBQiCgLulBft2STSr/PA9xPZ2jKlpmIcNJ/6Rx2mvqsR/7DgQBPlHUh+fQFtxkTyuLrLrhzjw+OnUz/9F/t4Kej3mkaPQxyfgyJd+7NQBgQSffCqNi//E09qK/xjv9BeA8MuupPqTjwBwNzbSVlyEys+P4qe8K26EX3QJKq0yyVHonV8LFtLU3szUmEksK1sJSKapu+eeHmm02Nv5YXkBAjBrwoFHNo0ZFE5ogB+1TQ5WbKv02V/f7MBs0FJRZ6Oy3o7FpCMx0h+16shzUldQ6Atb8mr599dbvKzniqpaSIy0IIoiLrcHrUaNy+1Bo1axKbeG17/diiDArXOGMSK5K9JtckYUP/yVT0NL1zPass3lfLMkjwcuGcU3S/LI3FVLRLCRW8/J6LHk6eFErVLz0Li7eGjFXAAGWOIZEzmSEeEZvJn5PtvrsvgpfwHnpJzuc6zL42Jezg8AbKre0i+Cgd1pZ3ON9CySFpS81/7N9nbyy5r5dU0Ru0qbePGmSQRbes/7FwSBswaeht3VyukD9ly2Wa1SY+gwG4zeLQp31l6OPRis3FrpIxYAbMyp5cpTvdu25dfz2cIcn761Ta2EBhz7Joe70x8eBnOAn/bUISjIiEZz+Iw/FPYPa34BHqvVq60wSkdxlI7JmVbUHtDWV+Fsbpb3+9U0cXm3tK7y6XWEhfma6Lm/yeTm37rMScrCtPw4TWSmkEBUTi3BmZK7u0qnI+WiOWg6VmGLNn7FphoBh07Ar303wymVCjyeXo31rCuXe4kFAHHjRyD08vBmbbOxs166STw67XZCTSHct+BpHK6uMS4acQaRYYF8ed6brCvfTHp4Kmbdgf+IDdek8VmW9HpR+TKya/OI8Y/ET6snNSSJ8PD+D4U6MXUym6q3UNpeQn55Pj9lLyQhIIYzBp1ItH+knI93cupUr/c/f+jpLMhdSlObZFy0smYVf+Qt56TkqYyJGUZlSw0nJU/1UZCdl11C0SefUfHOW8Sef67cbv/9ZwZcfeUBX09P37v+pm7VGpoWSyF5sTOm4hcezvZXuqJi3I2NAERNHkdwmD+EZQAZPuMEPTeX+nUbyH3l3wBEjxiCLqjj/MP8sZ94AhU/S7mAY955E12QhaaUgVR3iAxim4PUS86FS871GbuTsHNnk3LubGpXrCT7+ZfwZG/DWlSMx+FAbTLitklpE/GTx/T6f0Jh/zkU38NDgd3ZyubKHQwNT2Nj9RbiAqK5efKlXNw6G5VKRaDfkR+e+cQL0v9VEUiIC+6XMaeMiOGHpXnkljbKbSoBPCKIajVhYf5c/VxX2O5t54/gxANMhegrx8p3UeHIwNrq5NWvt/i0P/nRek6blMjaHVXUNrYSH+lPcWVXuWC1SuDF26eSHOu76h0ebCK7uIHgEDNrtlXw0XzpQeiR99bIfarq7Tzy3hrm3jCREal9W6Q5WIThz4snP8Kzf73J2UNPlv/P3Tr5Cm795VFsWHv8f/ju+q6Fr+Xla7hy3LmsKdmEIAjMHNi3yK3XVn8DwHnpszhhyJ4jP594bzXrd3r7W7323VZev3s6KlXvq/8Xh83e7/MKDR3O/2UF0NDaxH9mP0OIMWi/x+gLu0ob2ZxTw7j0SD74VfKxUQkwMi2cey4dw+PvriK7qAGj2Q9TR2SY2yPy6nOLehzvvv+s4qeXzjwk534kcUCCQVpa2mlALPDNnvo1NNj3tFvhCEL0eKj+4WcAfptk4ZSVkihQEapl0yAjNUEa5vzZSN7vvyEUlOARYMNgI2N3eP+NczeuomDIqT6T6CELvEsDxtQ4ufHrWqBWbjNlDCPsgotpsHvALv3Y6NyS2unQqfBrd3uNEfWPm6l463V2R2U247FacTZJXgP+4yfQsmY1YRdeTG2dbwTFzvocWtqtbKvdKbcFE4G6VcNtI67nq5z/cXLCdBID4rHgT02NdG5J+mRamzy00uIz5v7i5+kyy8quzQPgmvTLiDBKeX2d79mf6J3S3+ib7V2KT2FjKa+t/pBBQV1qd4I+0ev9jw+fyvHhU9lWu5P/bPmQhXmSceSCXUtZsEuK7qhoqGPWgBO93k83eTqmzduwbdlM6Vddt47m4vIDvr6wMP+D8hntTvnCrh+SXR9+gjY0FHuxVKZS0Olkg0JnRPzez2fwcHSxcRhTU2lyaaBb/zZB+vHSxyfI+0wnnop6/UbcTY0EnTprn6/Xk5CKoNNRtXQ5QoeAm/Tqm4htbYged4//JxT6xqH6Hh5sKmxVcnnETtICUqittQJq3EBNy5F9nfnlzRR1TFpuPSej3/4ugkcqE9m9YEJyTAA5pU0UlTUSZPB+vCosa6Smpn/Eiv3hWPkuKhx+thfU88nv2VQ3SO73Qf56Thgdy/SRMbz70w4yd9Xy68pCuX93sQDgxjnDCdCre/w+hgXo2ekRef3LjSzaWOqzf0CUPwUV0nF/rCkiJujIW+E1YOHJCVLUaec1tncEY7bY7V7X7fa4aXU7yKkp9Bpj4Y6VzMv5HoBE/QCMGkOvqQw90epqZXmRVI0mVr/n54+SaquPWADS323x2iKGDey5tOKB8NTEhwHw2KDG5n1udU0OthfWkxjpT1y4ea/pCm1ONy/Ny8Rqd/LYlWPR63wXpp0uN09/sIbaJgcf/SJF654+KZEb5gynpqaFVquDpEh/sosaWLetnPRE6R7935+6ykwLwO51ccormtBqjs0Flt4E5j4LBmlpaUnAC8DM7OxspcbQMULL2tU0LV0CQFFkV75YbaCGCZFjKLRuBEAokCZHS8aY2ZpsQCXC6J12/MeOo2XdWpJ2NVP481c0mARCRo1n04YFeBobGd/xTRE0GtmkrTthF19K0IyZPu0BemkVa8UIE7OWN3vtMw8fgTYsDGeNFLmgCQrG1VCPIWkgti2b5X4hZ5xF6Jzz0AZ73wRXla+j0l4t11/t5OFxd8l5WAmWOO4dc8ueP7x+QK1Sc13G5bzbUYbGpDHKYsHBIkDf++pTVkMuKkHFDRlXENpLXd6eSjh2srRkBSclTPcyRRQ0GmJuu5PmNauo+vB9+XvgamrsbZiDjqu5maLHHkYbHkHYBRdhSOrZKEh0u3Hk59GatwuVwYCg0dKatZPWbn2ib7md+p9/xH/MWFR+ey/nI6hUJD4+t8d9QSedgtvaQvCpXaGM2tAwBr70Ku1VlWj3weizE5Vej3HwEGybJWM2XUwsgiAg7MM5Kvz9qLbX+IgFAOMiR/XQ+8ikurGVpz5eD8CYtDBGpvbfvdSg9318SokLJKe0iU8WZMsrpJ04dhO6FRSOBqrq7Xz/Vz7b8uuxt3U9s41IDuWGM9LlSdot52Tw5vdb2ZRb63V8RJABa6uTsYMjOHlCQq8T2JgwabHkzw2lmPw03HH+cPLKmtmSV8vIlDBOGB1Lm9PNjS8tJaekEbvDidFPy4bsavIrmpkzdaDPirgoimzOqwMRDHo1afGHZkW7O51mh+3dUlxFUeTptS/LpQCD9IGcED+Vb3J/lMUCgIdWPEWEMczL0G9v5DUWyq+T9uJ59cNfUmj+1OHRLNtcDsDAaAt55c2s3VlFWlxgj5Pw3mixtzNv0S4mpEcwdMD+iQ0FFc3M/b/1Xm1PXj1Orjhjdzj59zdbaHd5uOv84Wg1Kub+33oq6qTFynmLdyGKIga9hvOnS2kYbo+Hn1YWUtvk7Y0xe5L355I+IJj5a4pZubWC9MRgKuvtrO4wyH38qrH8vKqI9VnV6LQq2p2SUFxWayUx8siPrutP+lolwQzMA67Ozs6u6N9TUjhc1C+YT+3X8wBo9VPj8FPx20QLgwscaAanctmQ88kJTIcfX5CPyU70463Zz1A0top4SyyiKFJXX05cXin8upQIIG/VSsaWdt0s20+YRNrJcxCdTppLCqh/+x0irroG/3ETes2hTg8dxITIMaxmPU5NCzq1ngHPPIeg1iCo1STOfZbcf1wLQOxd91D95eeEX3o5tq1bqP70Y4JPOx1dhO/EdldjAZ9mfd3je+5pInwwGRE2VDY/7F7n9mDhp/aeMN4x8h8sLF7C9jrpgTc5MImhob2b4QXpu8ILo02RlNu6cnptLjtba3f4lJwE+H/2zjowjjL945+17G7cNu7ubZrUjSpWpKW0QNFS4JCihxx32B1wOPzgONy1RY5iLVCopi5pk7TRxt09q/P7Y5JJtptUk7bAfv7Kzr6zO7uZnXnf7/M838d1/EQ0oeGYmpuoX/k5PaUlWPR65Go1nTnZqENCULqcmgty04/fYW5vF00+n32K6NfeshnTvmsn5vY2qT2k2/QZeF+8gKK7lluNc4xPkAw1TxaFVovvkqsHfW6w8/loqAODJMFAHRBwlNF2/qxUtFfx750vAaIxqk7rxe66fWgUGql11u+BtTvLpb8vnBw+rK/dfZgAcMvFSfh7OfLD1lLMlv4YyqIZUaxcV8jPO8u5bNbwGJodCUEQWLurAl9PLSmR3kffwc6fGoPRjFIht1ls1zZ3sSGzil93V2A0iYukQG8nls1LIMTXNvorl8tYfkkK3XoTuaXNxIV6UNvcRZDOGcURUtv7cNb0z/0WzYgiMsCNyAA35o7t90dRqxQ4KOVUN3Zx20ubSI32lgSKhFBPEsP7M3iyixt5YUV/wEgG/PP6cZIwcaqQy+So5Er0ZgNGs5Gi1hKCnAMksQBgtE8SLqrBS1pru+pp0bdK7Wn7aO5p4T/73qGms5YkrzhuTL6G/OYiPjggeoDdmXqTVacyiyAgl8kwWywYjBZkMsgpaQJgyZwY5k+LoLapC08XNfe9vpUt2TXI5TKWnnfsRsib9lezJbuGLdk1PL5svOQ10dFtZFduHf5ejkOKNqu3ldpse/jdHdx+SQqjo735auMhCirEbOE7Xt4sjVE7KNAbzKzfWylt25pTw6IZUXywJlda4D9xw3hyipuYlOSP6rAS+fhQD/y9HNlxsI7Jyf68uFI8bwJ1ToT4unD2uGAOVbVy24Jkymo7eH91LiU17UMKBt16EzIZaByGo+r/zOFEP81tQDjwfGxsLIA+Ly9v1rAdlZ1TjrmzUxILzN7ufHCWkmDnAPLCq8gL1zDLQ1TkXFy9rBLv/zrpHrydPBFcxYu9TCbDPGMSFK2UxkT2igVuc89GExCES3o6co2YTubt64f32xM5Gs4qJ65KWESHsZO35ltYnnoDSrf+hapMqSTsyWdAEHDw9SXorr8ConGc+/QZg76mIAh8Xfi91TY/Rx9a9K3cnnrjUY9pJJnon86myq3MCpk24u8lk8m4Mu5SPs79ghuTryHaI4JOU5ckGDgcpWWiTCZDIVNgFsxcEHE2JW3lFLeVMT/qPJ7e+TLvZH9MS9Q8ZoZMo6y9Ao1CjU9v1oSDnx8Ofn5oIqPQl5dhqK1BMBiofPE5ANTBISicXQi8654Rq6+3GA20ZfTfgASjEXNXJwrH/ht4Z9Z+ql9/1Wo/7/mXoHB2xnXiZNq2ZuA6dRru02ecctff48HBr3+x53ne8dcg2vnjU9JWZtVH/OqExfhovfFx9B60J/aZSlePkbW7xdTmN+89C6VieK8f4+J9+N8A86z0OB86e4w24yIDh0/0/GVXOWt3lbNoRvSQ7ef2FTXy2a9i96AHr0yz+xfYsaGj28gLKzIpqWlHJgMXrQqDyUKPwYyrkwOeLmpKessJnLUqZo0Jwt/LkYlJfkf9HWnVSimT53gisI6a/nnGmCO0Vlw8M4qPet3qB2YzvPFtDqG+znT0mHjgijF8td7a2E4AHnpnB+MTfMk+1IiflyNTUwKYNmrkhXMHhQNdpm42VW7lq8Lvmegvmk+HugbjrHJiVvA0lHIlo3RJ7KvPxkfrzbSgSXxZ8C0AD235N9cmXM5oXRIGi4GHtjxFt6k/rzG7MVcy4FbKFCyJW0i0R3+WZE5JEy+t3Mfdi0axJbuGjOwafD20GIwWzp0Qgkopx03pgJuTA4IgMCrSi31FjWzeX83VZ8ce9X++eX81K9cV0tHdf/17fVUOcaHuOKqVrNtbSXuX+Nxrd0+XshYaWrr5NqOEti4D+4saCfB24r4rUvlqfRGb9oux6Je/2k9MsDuHqtpQKeWSeOXiqMJgtHD5rGg8XNTSIh+gtcPAW98dkB6Pi/fB38sJf6/BRRmZTMbMMUF88ks+r6/KwWwRiA12597LRXP0yAA3nrtF9PaX987vSqrbYHSg9BoWQaBHb+LZzzOlErg7L035Q4m2J9ol4SngqWE+FjvHSG1TF5uzqsnIquaqubHDkmbZnS96C3hecBHvB1ZhaCtjWfLVPLJV/Df3RZVc1a68P8udS34V08f9nGxTonVJY/h86k80uik4J6MN32YTxETiu+jykz7O2SHTyW48SEZDJpE+sejNBj7L/ZrRukQSvGJZX55BapcSnePR06E+y/ua0rZyq20PTfjrSR/jcBDqGszjkx7ExeHUqOETA8aS5B2Pc6/KnewVj6fGg6aeZs47zINgMO5NX87u2kwSvGJJ0fVH1zUKDT3mHr4q/B4BJIHm1ZnWbTMd/MRoeU9REfrK/vpFfbnYgsnU3IzKa3jr6Qy1NbTv2I65swNLd7dknglgqKpCGyVGA9u2b6XmLetWcl69YgGIrQ79rr9hWI9tpHBKTsExMQn3s2aiDj6zne3/DJS2ldNmaCfJK/6MEJp21+7j3ZxPpMfPT/sXGqXYuWRexNmn67BOiL5Jm5uzw7CLBQC+Ho5ce26cVemB42FlCk4aJWF+/Qv2HoPphKNO1Y2dfLZWFAJe/V8WagcFKoWcc8eHMCrKGx8PLUqFnI2ZVdI+T368m7dDPfljVtraGQqT2UJjWw+ujg5s2leFp6uG9DhxrmYRBB57byeNbWKatiBAW1f/Qq+t00Bbp4FQPzGymhajs4nIjgTRQW6MivRi7thgnDRD1+zPGBOEUiGnsqETJ42S9m4jO3PraO0wkFMidtC6+QWxvFSrVvCv68dT1djJG6ty6Owxsf2AmGpeVNlGUWUbiWGeeLmNbFleuGsI2Y25/FS6DoCt1aK592T/cUwOHC+NuzH5anpMehQyOSqFivF+aTy/57/UdNbybs4nLIlbSHVnrZVYoJQrrbpW3ZV2M2GuIdLjbr2J5z8XswqfX7EPS6/pSm2vD4XuMLd/mUzGHZeO4tO1+azdVUFuaTOJ4Z60dxtxdRy8reXaXeWSWODqqKJLb6aivoOK+g6bsXvy65mY5EfWoUarRT7A1WfH4urowHXnxXPdefG8/f0BtmTXkF/egperhsUzowj1c0EGeLtbH/eb956F2SLw844yCipaya9oITHMk79clHRMXgOTkvz4akOR9DmuPid2UNPHQJ0TapWCosr+0mhBEHhxRaZ0/vXx6S8FdsHAzumjW2/ib29ukx5vya4ZFsGgp1hUY/c6tVDcVkqEWxjeWk/iPWM42JQv9Z91VGqp0qmo8FFxKFBNjNz2RhLg7Me5827FW+uFx1kKmtb8iNvU6Sd9jNBfk7W9Zjcp3glsqNxKfnMhu+syWRh9IasOrebHkrVcn7SEZO+EIV/nUGspGVXbrbYtS7pqWI5xuPAYxv65x8JAcUIhV/DQ+Hto1bcfk/gS7BJAsIutUn9W8GTWlPwKYJPNMRB1oNgXve6TDwd9vnVbBt7nX3jU4zhWTC0tlPzjb1aOZT5LrkYmk1H74XuUP/UE/n+5FXVQEM1rVtvs73n2uTbbfg8oXFyk7Bs7p5dWfRvP7voPAgK3jVpGvFfMaT0ei2CRxAKtUsOTkx+yaq/6e6Oqt7Z14fTB/UiGA4vF2r5JJpPxzM0TyS1tYXS0N869jttTUvzZvL+a1g4DGs8Tm3YdqrL27tEbzOgx88X6Ir5YX8Ts9CCCdM5kFjYQqHOisl40MX17VRY3zhv6Xmjnj0V+eQtvf3/Apm4bxPr0qsZOuvViOc3Ld0zFSaOks8eEo1qJXC6TxAYfd+0pFTEdNSruuHTUMY2delhWQLCPM+/9mGsz7u9XpePpqsHTVcPjN0zgrlc224xZlVF8XGn3J8K54bPJbsylw2htLBznaXvN7xNoQWyFvTD6Av6TKbai/iS33yT6wXF34eOoQyVXUtZWwdO7XgawEgsq6zt46J0d0uM+sSDA24mqBvFY+q5Rh5MWo2Ptrgpe6F3Uy2TwzF8mWYkrOw7WUtXQSXOH2EHsnHEhzJsURlVjJ19vKKKwshWTWcBJo+S2Bck8/eletuTUEOzrzDs/HLR6v+ULkokJtp7zLpuXwPkTQ1HIZeiOcj4qFXKUCrigt/RMEITjOn+1aiVTkv2lrDQ/z8FbmCvkcsL9Xcgra2Hz/mqp68JAIgNcCdQ5kxr9xxELwC4Y/O74cWCdj8xMdfPJO5tbenroOiim7/xoysHHzYfFUQv4euMhZM1p/G3GBZSWmfk0az9Xzo3hysTL+FC+4oivKU1+HcHnsitO+hj7UAwQKD7O/YJuk3hTtAgWVvb2sjVajLy+/32cVU7clHLtoMYvO2tE80aVXMm96ct/V3W5pwoHhcMxiQVH4vzwOfhovVlfkUFZe3/mQG1XvZWZozY2Dr9lN9KZnUVnXi55bj3siNcyY2cbAQ0m2htqcNiSQc27bxF8/4Noo09ucdWdn2dtbw5oQsNQ+fpS++F7ALRt2YyxoQFDVSUytZqIZ1+gp7gYwaBHprRfOv+ImC1mjBYjaoV6RCfLJouJBzMelx5/UfAty52WnXKRUBAE9tZn4alxZ3etODH00Xpzb/ryM0IsKK5uQyGX4efpiMlswfEIkcfDySwQa4SjAt2OMvLEEQRbv2dvNy1TUqyjX+7OYmSupUOP7xAT0aNR29ttasmcGDbtq6Kszjp6t3ZX//V1bKwPc9PVvLc6l/wy66iXnT8OJrOFZz7bS2FFK2qVAk9XtWQCNxhFA0Sny2dFS4vFgYtGpUKOr8eJnaOni4mJfvTozQR4O9FjMKNVKwj3d7UyJnVzcmD+tAiKKlu5Y2EKAnD/a1vZvL+axTOjjpjVcLIMXMT7OHojQ45GqcZLe3QTxnjPGP4z42neyf6YvfVZgJixMHDOGuwSSKou2cYg++uN1mUZfVx7bhxPfrQbEI1aByP6sNaXggD3vb6FMTE6ZDIZS2ZH8/qq/k4CyRFeLJopmg1GBbpx3xVjEAQBg8mCWiXO2yMDXckpbuLhXhEjLVbHsnkJIDCkueJQZQRH40Tu3zPTgiTB4Ej7J0d6kVvWYiMWPLBkDJ09RkZFeh+xJeXvFfus93dEU1sPq7eVgaoHuWM7DtF7aGoMRhDGn/Dktm37Vuo++wRLRwcNvo6Y1EqWJVzHcx/m0dopeg/sPtg/4cgsbODiqeFcHb+YIJcAjCbLoJOmU0GfWDAQGTLG+6WxrWYXHcZOvitawx1jbrIaU9tVz8bKrQA8PvnvUhq+neFHLpMz3j+NsX6pNHY388Ke/9JmaOef257lmamP4qQSJyYyuRzXCZNwnTCJbwp/5Jey9QB8N82dm75uQL91OzWbtgBQ/vSTxLz9/kkdV0ev8Z/vdddj6e4BiwV1aCgymYyQfzxC2eOPYWxsxFAlGun4Xn0dCkcnnBKTTup97Zy5NHY38eKe12nWt6CQKbj7sNTO4WRjxRbpb2+tF7VddTy89Sl8tN7ckHwVfk6+Nvt0m3p4Y//7tBk6mBE8hSkBJ37dN1lM5DUX8lnu1zTrrbuT3Dp6GY6q09+ybH9RIy99sQ+1SoGXm4bG1h5uuySZrKJGFkyLwEFlO8Hbdz4RAAAgAElEQVTMLm6ksbWHqCB3ckqaiQ12P+EF+rEgppvms7h3ojwU7s5i1LClw3DEcYejN5pR9RrS1TaJKcSp0d7MHCPWzv73m2x259Xb7DdtdADuzmp2HKwlp6RZcpS38/vHIgg0tHTj4ujAhz/lUdhrBKc3mq3EgiVzYogOciPA24nGth7e/PYAxdVtXHtu3Cmp2z+VKBVy5ow9eondBZPCpL9lwIREX37YWsrylzbx+j3Tba4pFotAQ2+2xXBhsVj4+/g7kXHs126ZTMY1iZczpmEUPxz6melBk22eX5ZsmyHbZzYZpHNGQKClXc/NFycRFejGEzeMR6mQ4+Y0eJmBXC5jXLwPOw7WSdsEAel6syu3zmr82Djb8mSZTCaJBSAKOwNT+a+aG2v1/OnGz9ORGy9IwMNFfcRxyeFefLGuyGrbohlRNhkSfzTsgsHviPfX5GIRBLSp65FbBMYc7CI3vJhXvt3N8gvTjnvyKFgs1H30AYIg4HL++byu3U6MeySl5SZJLBiMbzYV88/ocWgtSu55NYOObiMzUgNJj9URG+ohmYKMFFMCxrN5QDnBOWGzpLT3axMvJ6uh3+ykpK2M5p4Wq8hdX8vCdN/RIyIWFFS0kF/ewtnjQkakdvb3iFwmR+foxZOT/8Ft6+4HoLi1lDjPaJ7Z9Qo1nXVcm3g5Kd4JklgA0KOWkR2pIanIWhwytbWhdD0xM7GekhK6DuQgc3DAdeJkGzNFTVg4Sk9PDL1eCp4XXITr+Akn9F52fh9YBAsPb+235TELZn4t28j1SVcOOR7E87rH1INGeXw1sNtqxOjO9KDJpOqSeWnv61gECzVddXx4cCWXxy7gy4JvMVpMKGUKlqfeyP8Kf6CgRYwYfZ73NRbBwvSgSSfycfkk90t29GZZDWRW8DS8tZ6D7HFqMZktrPhNrNfXG81S+mxfLW6wjzOTk8UIm0UQ0BvM7C2o5+3vxYiPT2+P9plpQSN6nF5uGt6+f8ZR73luTn2Cgf6YX/tQVRuPf7iLQG8nFkyPIKe4CRmiJ0PfvX5gBFWrVtKtN/HItWMlgSLY14WckmbW7ChjwbSRK834vfPNpkN8m1HCFbOjmZ1+6rxd+pzrjxWzxcKTH+2muLrfelqtUrBkTgwZWdWMjfchLUaHq5OD1XzQ18OR2xYk02MwnXDE9o/IzDFB/LBVzNpdua6QK+fGWj3/654KPltbwKIZUZw9Lvikss7mhs7g59J1xHhESq0WjweVXMkYn5RBO04NRXVjF2oHBY8tHYvZIiAISPX8x3IeLJoRxY6DdQTpnPD1dJTEArVKgd4olrWMjvLmstnRxySqTE72Z92eShxUcv5+dfqIrxVOhAmJR+8+5e9tLUK/+8DMkTqcMwq7YPA7IbOggZyaEpL8f8OxQGDC/g4c9QIJh3r44PwvaO5IwNPl+CIp3fl5WHp6cJ06ja6ZY9Hv3klPu4b3tom1YNefH29VZxTq5yIZSeWVtyCXyySDkHV7K1nX29bknHEhUmrSSHBZ7AIrwWBW8FTiPKIJdglEo1RjtpjZVZtJqGswpW3lbKzcykWRYs15eXsV1Z2i6c01CZcN2zG1dOhpbO3BaBJTBAF25tbx6HXjhu09/gjIZDKWJi7h3ZxPaOhuoqy9gsoO0Q03sy4Ls0W8CUW6hXF32i0YzAbulT1Mp6YNt04zERUGHEwCZf96lNDHHkfheOznfHdBAeVPPyE9dkxMksQCQRDYU7ePKPcI3NSuOAQEYmpqQqZU4nnu+cP4Ddg5U+gwdlLUUky8Zww/Fq+1eb64tWzIOsj3cz5jd90+XB1caDO0o1GoeWrKw6iOkMZvtpjJqNpBZn0WlR3VJHsnsCjmIhq7rVPGS9vKeWrn/1ltu3vDPySRIt13NLtqM/lf4Q8kecXhdQwL/OrOWrLqDzAtaBKNPU1WYsG0wIlMCZxAgJPfGWG8CJBd3ER1Y5dUX304+4samZzsj8Ui8O6PB9maU4N2gJlgXa+hV+wpiPgcy6TX3UVcILQeY4aBRRB49nPxPlLZ0MkrX2VJzykGCJwJoR5kHWpkxuhALpwSbrMADentYf79llJmpwcPaVr2Z2bd3kq+zSgB4NO1BRwoaea68+JwOcHvKr+8haKqVmanBUuLs7qWbjq7jdQ2dSEIoHPX8tHPeZTXdaCQy1h6fjxjYnRDRlt35tZxqKqVnOImKuqty1AvmxXFlBR/pqQcuaxSjJoeOXL6Z8PDRc2TN07gwTe38dueStyc1cybGMqvuyswmCx8u7kYEMWE4uo2br74xDMM54XPRaf1It03dbgO/4iYzBZqmrpEk0CZDKXi+K/tnq4aXr5jKhoHBUqFnMyCBlydHAj3d+Hjn/ORy2Qsmhl1TKaCIAoN/1o2/ugDz3AUcjm3LUjmP19n4er458ncsgsGvxNWbNuFZ+Qm5v3P2vjIo92MTC5wsKqGybERNvtVddSwqmg1VyUssommt2wSnWRzQh2oqRJdW/MOKLFYBMbG+TA52V8SDMbF+/CXi5Iorm7jXx/sYnNWtSQeuDg60N7VPxFas6OMhTMiT1g9NJktKOSyISevMpmMeeFz+b74ZwAcVY5Ee/R/9rF+qUS4hQHw6LanaTO0IwgCO2v3UtYmRo0XRl9o1aP2ZHlhxT4bR9iyWluHWDtI/YS3VO8gu7HfQby2q57mSjFV+8r4RYDoo3D7mL+w1nsD2xpyUJgFbl1Rj6m5ieZffsL7ovnH9J6CIFiJBSgU+C3t72ywMn8VGyu3EOUezs0pS/GadyHG2hr8lt6I3ME+yT6VZDccZFftPuZHnY+benhbwmU3HOS1/e+Rqksmq+EAJsFs9fz8qPNRypRkNRwgt7mAqs4aq1rRVUWryajaTqdRTP1tM4jXwB6znq8Lf2Bx7MUYLSY2lmwnUBWMq4N4/LWddfxz+3PS64S4BLIwWmxr6aX1YLQumU5jJ3Ge0Xx36Ceb47YIFlRyFY9OvA93tRsx7pF8mvcVu2v3MTfMtm3s/voc9tTt59ywWVR11vJ29kcA1Hc3sKXXoXtp4hUkeSfgIFedMUIBQE1TF69+LS6Qrz8/gfZug5Whmaujip25daRkVVPV2MmW7BoAuvTWwsLs9CBch0i3PdV4OB9fhsFnawvQG/rPzZljAvltT6XNuAmJflYRscPvucG+/b+fbr3JLhj0YrZY2F/YyG97K8kpFnvRK+QyzBaBzMIGVvxWKNZWHwfZxY28sKLf9f2LdUU4aZSYLILV/9L2WASpBVxUoBvpcT5MHxWAg0rO3oIGDEYzbw5oEQdw44UJ5Je3MibGm6Tw4e0e9GfDz9NRaiP4v42H2Ly/ivqW/oxGZ62Kjm4jWYcajzkjRBAEfttTiUwmZjGA6L81KeDUBZBe+SoLs0XA3+vkSrIG+luMHmDid9XZsYMN/9MwJkbHPYtHo/M4/eV7pwq7YHCGU9XQySPv7kA1ehPp+bY1+xYZyCwCH6zNtBEMBIuFjLefROVg4f7Gg7w0/QkpAmZqbaFj+zZaneR8bd4H1TIEswJXSyAP3zbepq6pL1LTl+rZJxbMHBPI0ouSeXVlJltzaqTxe/PrSYu1rWk6EoIg2NRkJoV7cselKVZRFYCzw2YS5xmNdpBU4L709z5H2m5TDz+VruO7Q2ukMSEuw5eq+tHPeTZigZgqenq8HfoQBIHa5m4q6zuIC/UYUVOf4yHQ2R93tRuVHdVUUi1tr+gQ24EleMbi49h/Y4p0DyPcLYSnd75MRUcVr1/qzfK1Fpq+W4XSRZwQu00764hGhO3btkp/B9x+F07JKVgEC3lNhfxY8guFLWIkobClmOd3v8rfx99N+L+fHfL1BEEgt6kAAYEErz/3jXO46DB0sqpoNVuqRUOk4tYSHhx/N+oTSN8cjMbuZl7bLxpa9plH9THJfxzzo87DsddTw0nlSG5zAXvrxHEeajda9G383NsWayD3pS/nud2vsrFyC5sqtyL0/u5TvBO5KeUaALIa+zO1pgVOZGH0hVYGrjf01p/WddVLgsFY31QCnPxYdUjs0rEw+gJJbOszlc1vKaKjsJMNFVtwUTmT7J1Aqk8Sb2R9AMDO2r1Wx9onFqT5jCLNd/RxfHsjjyAI/LitlK82iGUXrk4OxAS746hR4u2m5dnezK3R0To27quSxGxXJwceXzaemsYuNA4KZHIZBqOZcP8TK1kaCfqEi5YOPWW17QgCrPitgFlpwaQd1nO+x2Di117jrRAfZxbPiiY+1INAnTMuQziaD0XAgMVCt942U+P3QlePiXd/PEhyhCdTUvxt5gPHsn9FfQchvs5UNnTyzvcHqWnqr/e//vx4xif48sHqXDKya9iSXcP5E0OltO1uvYmiqlY8XTT4eznSYzCz7UAth6paifB3ZW9BA9m9wgOIZSOtHYZBs2MUchkKuYxb5ieREunN3oJ63vg2B4PRQmFlK4WVrWQVNeDmrJbEMICJib6SEd2EBD8mJBw9ddrOsTEqypt9RY0AVmKBTAYPX5POVxsPsf1ALX95bgNuTioWTI9kfIIvz3y6F73RzPXnxePrqUWlVEhBtYH0iQanAotF4PEPd1HSO0dPjrALSiNFYvjpL987ldgFgzOY1k4D/3h7O3KXJmQKA4lFPchUKgRjf89cuQC3f17PR+fU2OzflpPFqGwxI6HJVUFxWxkxHmIdY8t6ceJbrVNhbvfER+NHVamauclBUv0jwOxe19DR0eKkxkmjws3JQfI4uGxWNG7Oam64IIH0WB07cuvYfqCWVZuLSYn0GrR/b25pMy99sY+/Xp5KkM4JjYNYe7k5q9rGwCm7uInCilZiQ6zdZOUyOeGDdD8YiFYhigndxm4rsQAg3G14zMy2Hahh3WGRn1vnJ/HLznLyK1ppbO0ZsR6/FkFg5W+FNLfrWTYvwSotTG8089QneyRhB+DxZeMJ8D799YsapZqHJ9xLWVsFJW1llLdXolFqpDaX04Im2uwjl8lZnnoD9296DINKjnJMMqZf11H36ccAdOXlEnDzbUC/c/nAqGnT6h8A8Dj3fJxTRtGqb+PJHS/atDkCqOqsIbMui9E+yVbbN1VupaKjmjSfFA405kteC5fHLiDcLRRBEFhdspa6rgaWJV9l41hs58h8eHAFOY39keSGnia+zF/FkvhLT+p1i1tLKWg+xJ46MfrnrnajRd+Kq4MLwS6B6LReLIy+0Op86ROBVpesZXXJWkJdgvFzEgXQyQHj0Gm9+aboR+QyOaGuwcyPPI+vCr+XxAKA/Q05rCpazVjfVHIaxM/1z4l/O6Izto+jjpfP+jdymVw6nvH+adR1NVhlUXmo3XFWOXGwKZ+DTfkANOtb2Fi5hY2VW2xeN8QlyKpLyYWR5xzfl3gKyMiqkcQCgHsWj8ZRI05RdO7919CZYwLZuK9KejwnPQhnrYqooJHrhnCyKBVyXBxV5Ja18Oh7O6XtuWUtPHhlGj/tKOOCyWGE+LqQWdggPX/nolHS/XhGauBxv69MJmN8oh/bc2r45Jd8/n5V+sl/mNPA1pwa9uTXsye/npXripidFsS+ogYEAVIivbhkkNaZFkGgsr6T5nY9n/9aYCUQ9DE1xZ9JSX7S/OL6eQmEB7jy8c/5/P0t8X50/sRQDpQ0U1wtzqU8XNQ0t/dnimRkiXMvrVrJuHgfUiK9SI3W0dqhp6api26DmYgAV1y0YiaPyWyhx2CWIrep0Tpeu3s6tc3dmC0CX6wrZH/v4hXASaNkVloQ8yaF2T2RRojpowPQG82s+K0QEB37/3F1OkaTGW93LUE6J7ZDb8tJPR/+lMcvO8ulRfnD7+5gYqIfN1yQwI6DtVav/ekvBfh7OhIfdmoWl9WNndJxXTw1nHHxtga6duycCHbB4Axm8/4qkJlxiN6Lb6MZj3YzzhPGYQjxo3P1T3ikjKE9Q+wre8Gm/bDQev+y77+UKtYmZ3bSOKu/VrahvBA5sD3SC0PuOCp6HVvHJ1hfXC6bFc38aRFW5koPXzuWe17NALC6gaXG6Bgd7Y0gCOw4WEf2oSZSY3RYBIH65m7cndVsP1jL+6vFyfOTH+3Gw0WNQi6z6hl8waQwNu2vkhyli6rabASDY0EhV+CsciK/xdrN9PqkK4etHOHNb8VUweggN+5aNAqTWcBZq5IiRG99f4AHloyx2qemqYu8smbGxfuiVMiPuf4LoKPbSGNrD76eWr7LKOHnneUAOGqUXDQlnLv/kzHkvv94ezuv3jXN6n95ulArHIj2iJAWQb+WbZSeS/SKG3QfZ5UTC6Mv5MuCb/mmezcDlzwdu3fRtOZHPObPo+TvD6BwcUGuVmNub0cbG4uhqhLHhER0l4iLz63Vu6zEArlMLtWIA7yd/TGPT34Qd7UbPaYe/rvvXYpaSwDYXLnN6rg+y/va5lg3VmwhxTuRb4p+xFPjzpXxl6JV/jFT18wWM836VjqMHSfcVaCwpVgSC+I9Y5geNInX979PeUfVUfa0xSJYeD/nMxp7mrl11FKe2/2q9FyMRxTLRy+jRd+KRqEZshtAX/eOPkrbyyltL8dJ5chlsQuQy+R4aT0JcBKjfNOCJrGnLovitlKUciVXjVrAe3tX8nPpOikrwUXljOcxtE0cmHkA4KZ2xU1tHS2XyWSEuYaQ3WjbAxogVZfMsuSraNW3oVVqUMqV7K7dR1l7BQui5p1RJQiCINDcrie/wrpbg6drv3Dt5aph4VmRRAa4EuLrwpz0YH7ZJV770o8zk+104e6spr3LaLP9yY9FA8zd+fW88dfp0r3jn0vHWYn3J0pjq+jnMNCd/HjRG8zI5aBSKmho7aa4uh03Jwd259Vz/qTQYSl1yCxsYNO+KhLCPIkNcaeqoZNx8b6YLRa+3CDew6OC3CisaOW7LSXSfuV1HahVCgorWzGaLFw5N4Y9+fVsya6x6hrQl1rex00XJtrMdwCmjQpgx8E68svF87HPFK+PPrFgQqIvkxL9aGjrobPbSEqkN8G9nhEAbs5q3Ab5/ykVcpy11vd8mUwm9X2/dX4yX20oYl9hA3dcOmrIfvB2hg+ZTMbZ40LQG80cqmrjtgXJVnPbUD/r0ji9wSwtyvvYmlPDZbOi2LxfzJq8e/EoHJQKnv1sLy9/lcXNFycSEeDG1uwazkoN4LVvcnDSKlkyJ4bKhk7eWJXDJb2ZC9mHGlHIZcclMpjMFrZk1/BtRrG0bdYIm77a+XNx+lcOdoYkr7wFhXcVMqWRC/aJNXCOcfH4T5kGcy/C0tMtCQbunT388NZKFNUZeFx9GVH1oC4ql15L12KirCAP/NNp/OE75Jli/9TW9jQeWzqe91fn4u7sYOOcKpfLbBaYHi5qlp4XT4ivM4cjk8mYlOTPjoN1vP3DASIz3XDRqtiaU2szFrBS6gH8vRyZPy2C+dMiaGjt5r7XtvLl+iLiQjyICDj+FNNgl0ApAhfjEcUto5aikg/PaT/w2G+Zn4xmgOnWtFEB5Ja1UNfcxe68esrr2rlgchiNbXqe/nQPrR0GPliTR1SgGw9elYalNyp+pPq43/ZU8PHP+YM+tyGzig2Z1ouryUl+LJwRRVNbj5Qi95+vs5iU5Me4eN/jEipGmnhPMcX64sjzjijmjPVN5cfiX6jwEX8P2tg4VD4+tG3aSMOXKzHk5mCsq8VY13++6cvLANCEi+KEwWyUFqepPimk+4wi1jOa7dW7KW+vJLe5gBZ9K98WreHqhMVkVO2QxII+Apz8WJq0hHXlm8io2mFznOsrMlhfIYo3Ze0VBDj7c374nBP8ds4cTBYTPxT/QkV7FQea8gCxx3RJm/gdL4m7lEkBYzFaTAiCgMMRTAD7MJiNrMz/BhCzNdJ8R6FVavFQu9Pc00JNZy0HmwpI9x2Ni4PtNWcg5e2VVoaB9256VPpbo9Bwaa93iafm6ALk/em389r+9+gx6zGYRfGy09glnZ8D3aqVciV/Tb9Veuzp5ciXOT/SbugvVYpyDx/Whfr8qPNo6GliSdwlqBVqUZjtbqCuq4E5IdMBrISGsX6pjPU7NYZbx8PAMoSBOA6478hkMs6b0J9RdvnsaBLDPfDxcBzRtonDSZ85VoC3k9T14XBuek70FUqJ9CJQNzzZYPdelc5N/xa7CL385X7iwzyYcxydAExmC3e8sgmD0cL8qeH8vLPcKtX+l13lPHvzJLRqJev2VhAf6jnovdpiEQbtTd5jMLFuTyVfrBdFgb0F/RkWXT0mdhysRW8w4+2m4cEr09hbUM/+okbqmrsZG+/Dp7/kW/Wb78sMUMhlOKjkGIwWRkd5s/yS/oyxI/0OlQo5DywZw77CBrbm1CCXywjSOXPOuBCa2nsorekgwNtxxLoNqJRyLpsVzWWzokfk9e0MzYWTwwfdnhDqSVK4J9nFTcwdGywFahaeFcmc9CDu/k8GnT0m7nh5s7RPn7fE5bOj+fjnfDZn1bAzt46MrBoqGzqkTKK+DBWAN77NIUjnxAsr9yGXybjmnFgcNcpjKu9987sD7MqtQ6mQMWtM0HGZEdqxcyzI+tJ3R5L6+vbTW8z9O6SyoZNHv/4Gh6h9BNQZuHStqHaHPvYE6sD+1MTGX36iccVnVvuW+6gIrrONZDT6OjPmlgcoefQhZILAIT8t6kv+zozUk1MhdToX6uv71dbWDj13DRLpDvZxprxOnEBfe24cMuC93myDtFgdRZWt3LYgRZpsCILA9U/31ww/cu1YG6X3aKwp+U0qR7gn7VYijlLGcDz8tKOMFb8VcuHkMC6eams4ee9/M2hsO7rJ1YNXpfH5rwUcqmrj9ktSpLrdgVgEgftf22LzeimRXjS29VA5wDk5wNsJo8nM48smSDcMo8nCTc+tl8acOz6EMTE6gnycUSnlZ0R7mx6THo3y6BE1i2Dh5b1vUlNZwD2T7saxppnKF58bdKxjUjKuEyfRnZeH57wLadPCv7Y/h9FiItg5gAfG3WmzT6exi/s2PUqkWzjj/cfwae5XgOi9cF3iFZgsZvydfFD2Ck+VHdXsrz9AaXsZsR7R9Jh6JEPOPrRKDf+c+Dc0SvWwmm2earZX7+bDgyuOOObWUdfzRcEq6roaGOubyrWJlw85tr6rkad3vUy3SYyCvjLjKen7+TT3SysxZqzvGK5NtO5scqi1FJPFyKqiNVwQcTYr8v9HXVeD1RgXlTPLU2/AQ+0+ZEbBkajurOXF3a/Raeri2oTLj2nRrdO5cKC0hBf3vIbBYmB2yFmM8Umx8uawI7L0qd+kv8P9XYjwd8MiCH84U60XV+4j61AjoX4uLJuXQG5pM5/8IgrA8aEeHCwVMwBHR3lz88VJwzbZ1+lcuPrRNVYC94vLpwzZf/1wdufV8er/so/5/fy9HHnihv42tBaLwAsrMzlQ0kxkoCu3XJyMh4saQRBo6zTw4sp9lPXOC3w9HfH3dLQqy+jj2nPjmDYqwGb7nvx6NmRWEaRz4rc9leiNZpbMiWFCoi9OGtWQnU7snHoOnyv+nhAEodcHRMa/P9lNWoyOi6aIInBrp4H7X9uCwSRmKZ4/MVQqkzGZLdz47HrAtpzlWPnrZaNJGCTbYPP+at790TrL7PlbJ/d2xLBzJH7P5+JIo9O5DHrBtGcYnIEUVbbyzOe70KZkElRl4OL1rQA4p6ZZiQUAnrPnkvvL1+ia+i9CA8WCT8ZOQ6fXMXf/V3jVdlDw+v/hIAh8M0lHtXkWz48+/rrIo+HmrOa68+KsnK3nTQpjwbQIdufV4evhSFBv6p6rkwPbD9Zy5ZwYHA8z5ZPJZJwzPoQ128Xo5U87yrjxwsTjOpYZwVNo7G6ixdBKsLPtZONk2HGwFpls6NrSI4kFl0yPkKJqT360W9r+8lf7CfB24vHe1jN9gt6BkiYa2/SE+7tSWd+Bk1bF/UvGoOv1R/j810IsgsDstCB8PLQ2EySVUs7VZ8fy4U9iVHj19jJW936vHi5qHl82/oilCpkFDaz4rQCDyUJzu54QX+feLJPhc7E/FrEAxPKBKPcICloO0SjrwjshEZ8lV9GxZw9dB3OsxrqkpeM6fiKu4yfSYezkrcy3MVrE6Nj5EXMHfX0nlSPOKieKWospahXT+zw1Hvxt7J2DTjwDnf2tnPQFQaDL1E1jTzPXJlzGhootfFP0I/duegQAVwcXHp/0oE3q+ZlIXVc9Gyu3kqpL4YU9/5W235N2K2tL17OvIcdmn1f3vSP9vbN2L9OCJklCXX5zIa9kvo0gCLx01hO8k/OxJBZMD5pkJabMjzrfSjCo6uw3yATRxPD5AeUGr2S+BYCLgzP3jLmV5/e8io/WmztSbzqp79rfyZdnpj2KyWKSRKJjQefoxeOTHwT4XYtEI8nhRny3LUj5w052TWZxMaFSyAn0diLQ20kSDO69PJVfd1dwoKSJv1w0fGJBH0vPi+f5FZnS47te2cwTN4y3ipJ39Rh58M1tJIZ7ctmsaDKyavD10LJpv/XvztVRxfTeecOUFH8eenu7tFACsff7wdJmLIKAv6cjDa09HCgRxZCiyjapnHEgSoWcaaP8mTEmiEBvJyyCwPq9lfy6u4KpKQHMHRs8aHYCiG7lY2JEv5hLpkdiEQSrdHK7WGBnOJDJZNIc9V/XW7cGdHNy4JU7p7H9QC1qBwWJAxb3A8/Fw8WChDAP8spaSIn0IinCi49652d9+/VdM9754SDP3jxJ+g1YBIEv1xWxZkeZ1estnhn1h71+2jn92DMMzhDMFjONPc1k/Pobqh0bkSsMRFX0tyqUOzsT+o9HUHnbGqltePo+/AvqbLa/MTmFRxffRlV1M7J/3y9tzw9WszNuATfPmTos9XFDKXUlNW1kH2rC211DeqzPCRv2mC0W7nx5M509JhbNiGJWWtBpT7WqqO/g4Xd2kBLpxZ2Xjhp0zOe/Fkipa3Eh7ujctSiVcupbulneWyP3yLs7pQ4LqdHeUjrmlXNjmNTfqkkAACAASURBVJTkxytfZVFS00a3XkzBf/CqNIJ0TigV8uP+PvtqhT9Yk0fWoUar55bMiRm03s1ktnCgRDSpPBxXJwf+df24E+5XfTIMjHQvTVxCmu8oWjaup+7D963GRb/1HjKZDItg4dGtT9PY00yKdyJLE6+QOoYMxj+3PUdtl/ibCnIO4NKYi4hyHzxd8Wj0mPTcs/Ehm+0Loy+0WSS36FtxkKskt/7TyabKrXye9z+b7VMCxnN53CUA9Jh6KGg5RG1XPRqF2srPYaAvhJ+jD75OPtR3NVDVKaZgRrqFU9RaTIp3ItclXoFSrrBZWNd3NbK2bD176vZjsBh5aspDaJVaOoyd3L/psUGP+7705YS6BmMRLKdtoW6PXhwbu3Lr+O83YvR6Tnowl8/+46Zh//vj3RRUtBIb7M79vb42Da3dmM3CiJZV9J2Ln/ycz697+o0vl82LZ1KSKHS2dOit/G8GZgMChPm58OBVaazaXMzYOB8robi9y0C3wUxrh56c4ia+zSgZ9DjOnRDCoco28sqtvSqmjfJnyZzY035PtzPy/FmviwN/e2oHhdRm8/9un4JWrUSpkKM3mvlpexnfby1l2bx4ogLdyC5uYk++WIJzwaQwLp4aTk1Tl1R2AyADrj4nlqmjAs6ITNHfC3/Wc/FYsGcYnKEYzEbeyv6QusIcZuxsJ7HBtpRAt/hyXCdPReE4+KTCJPRHadq0Sly7xcfnjp6Fi6MDIUFePBcwk8VVYurn7vAU/nHJ7EE7GAwnYX6uhPmdfGsrhVzOgumRfPRTHivXiZH0gfWsI4VFECgobyHc3xUHVf93pTea+WCNmD0xJdl/qN1ZeFYks9ODUKsUOGlVg17MZ4wJ5KOf8kiP1XHL/GTWbC9j5bpCPv45nxW/FWIcELlxc3YgMsD1hCMmMpkMT1cNdy0ahSAIFFW10dFl5OWv9vPJL/mU1bZTUtOOzl1LQpgHMUHurMooljpXpER60dKhp7PbSFqsDz/vLOeBN7Zx56UpRAW6ndJIzsDU7pX53zDGJwV1kFiXKwsORC6Xo/X0kY7p7ayPaOwRo1w3JF911IXkKF0iP5fWcUHEOZwTNvOkjlWjVHNH6k28vPdNKxf9Lwu+5cuCb5kcMI7FMfOp727gX9ufB+CBsXfgrfWUjBItgoUvC75jQ0UGj0y4b8RT22s76wYVCx4afw86bf97a5Qakr0T6KsO7jJ2s+rQalxUziyJX8jr+98HoKarjpoua1GzL3vjkuh5Q3od6By9uDzuEtRKNb+WbaSkrZwdNXvYUbPHZqxKrkIlVxLQm+1hj+qf+TR3iBG3q8+JZVrK8GaAnWn0GRgGDPAm8HY7dUaoS+bGMH9aOA+9s4Pmdj1dPSaKKlsJ9nFm/V7rTj8DxQIQzY+VCvmg3QhcHB1wcQQfdy0hvi5kHWqkuLodhVyG2SJe71ydHBgb58PUlABeXJlJoLczYX4uGEwWFkyPsC907PyhWTI3BmdHFas2F3PehFBUCjkKhcwq2KJWKbhwSjgXTukPTEwbFYC/lyP7ixr5bkuJldknQGKYB3ctHm3//dg5JdgzDE4D++qzWZm/iktjLmJP7T7qM3cwv7fsAOBQoAZfwZHApHG4TZyMOvjIBkVbCjfi/dS7AJT46QirERd4Ls+9hL+76MpdXN5Iy7/vx6iU4fXAM0QGHH/XgaE4VUrd+sxKPlyTR1KEJ3cvGp4e4kaTBaVChiDAZ78W0NDSzS3zk1HIZbz340Eyevsg9y3oLRaB/3ydRWZhAxMSfFl2QcJJXaxNZgslNe2SEHC4/4OzVsXzt07mUFUrzloVgbojm76dCE98tGtIB22Ng4IegxmFXMbzt07G2VEFAnT2GK0MfgJ1Tjx8zViAUxIpOjzCHOoSzD1pt9BWsJt/F6yg01EUeGLcI3FVu7CrVkzH9XH05pEJ9x319c0WM1WdtQQ5+w+bEGKymDCYDXx4cAVZDdZ1h5fFLiCzLovc5gKr7RqFmuen/4sNFVskY0CFTMHLM/4tjbEIFrZX7ybKPYJDrSUkeyecUK0+QLepm3ZDB49te1badnX8Yj46uJJrEi47pvp9i2DBLFhQyZWYLWbWlPyKXCZnbdlGesw9UqcLB7mKZO8Erku84qjf8d66LN7O/khqidjHRRHn0m7sYFbINPQmPUq56ohtC08V9ujFsfHVhiJ+2FrK/VeknlAnnN8T7V0Gvt9Syuz0IHTup04oOPxczClp4vnPM63GuDiqaO8y4u/lKHUWmJjoS0V9J+dPDD2p1mx2DwE7ffyZr4t9QZoIf9chy2uG2u/7LSX8b1N/9wNfDy2PXDfWymjbzvHxZz4Xj8ZQGQZ2wWAEEQQBAQG5TE51Zy2P90YPB6LWW7huVRPqAZHk+rv/yeSEY29PJggC+25bSpdGTltAPBEHxRTPsNffxkHZf0H5aUMucpWSOZOiTuJT2XKqfnhdPSZue2kjkYGujI3zZfqoANq6DGgcFMedFm+xCLy2KluKnh8LDywZw4sr96E3mokP9eCuRaNGpC/yxz/n8dseMeJz7vgQLp0xvP+vw9Ebzbzzw0F25dqWtYDYavPac+NQq6wzUtbtreTrDUWSY/bFU8JZvb2M6aMDMBjN9BjMXHde/IgJCL+UrqekrZzM+ixp22XJF/J51reDjo92j+CmlGtOe3tDi2ChuLWMN7M+INojkr11+6XnNAoNPeYeq/FPT32Erwq+s4qqp3gnclPKNQDsqtnLewf6jU+dVU7cl347v5Stx0Gh4tywWcf0mbdU7eST3C+stk0PmsyimIvoNnWf9PfWYeykoPkQo3VJWAQLMpnsmLMAGrqbeGTrUzbb70m7hQi3sJM6rpHAPhk5Np79bC+5pc383x1Tpb70doaXw8/FupZuHnh9q804bzcNnq4aqZ3gUG0H7dg5UezXxROnvctATkkTmQUNzE4LJirI7XQf0u8a+7k4NHbB4BTTYezk1//8A4XRwpy7n+XnNW+g+y2Tb6e70eoiLuK9NF4k5jqQumUfhRHjafPwIyAmjGmzjr/11Qs7/4tWqWHMbgc8tq3FJJeR8OZ7w/2xBuVU/fAO75qQHqtjV++C/4rZ0cw+jlZRP2wtGbSV1+FcOTeGXbl15JZZ113etWgUyRFex/x+x0u33kRLhx5vN82Il44cjiAI5Je38PSnewGxL3RarK13Rh87Dtby+ipb8zsQU1EfXzZ+xBYDFsHCb+Wb+F/hD4M+vzRxCS4OTmRU7WBe+NnoHEfuf3aiDOzksTRxCcne8eyty2J7zW7ymgutxt6QfDVvZX0IiGaLXcZuBASrqPvhJHnFcfOopUc8hoEZG+5qN8Jcg/FQuzMv4uxjNqMcSQRB4LZ1og+Lr6MOVwcXGrqbeGjCX1ErTr2HxtGwT0aOjkUQuOX5DTj1ZlHZGRkGOxf7OlOMjvLm9oUp6I1iFllTWw8PvLENgKf/MvGUZkLY+eNjvy7aOVOwn4tDY/cwOIW07d5J8bv/JV4v6iSHVrxH7DrRCX98Vic74l2YusGBjkAd8YVbABg7fxa6+JgTfs+7x94CQKl7Dbn7smlKnU7CSX6OMw2ZTMbYOB929kbCdw3IDvh0bQGJ4Z78srOcOWODaes0sO1ALYtnRtmkba3fWzmoWODtpuGvl6fy7Kd7SI7w4rwJoXi7a0mN1vHBmlzau4xMSvLD00VNUrhti5vhRKtWHrFrwUgik8mIDfHgsaXj6Og2Eh965FThUZH9Ne0DDX0A2joN/O2Nrdy1aDQRAa4UV7ehcVActYd1VUMn7s5qtGrFEdNZ5TI50wMn0aJvZX15hpVHAECar2hIGeMxslkaJ8O0wImSYBDvGYODwoHx/mmk+iRz14Z/SONCXIIYrUvC1cGFNkM7lR2ie7kMGWqFA5fFLiDEJYi9dfut2jpmN+byWe5XklHh4bToW/n4YH9mwROT/z7kseaVNfNtRgkXTg4jMtBtRDJsBkMmk/HA2DvIrM/m7NCZkpHlmSgW2Dk2vtpQhMFkIcy+KD3lXHtuHNWNnSyeKZpM9mWP+Xg48vIdU6lq6LSLBXbs2LFjR8KeYTDMmDs7Kbr7djCbjz64l7a5l5K+6PxhO4a65i683bTHVSd1MpxKpc5iEfhxWylfbzx6dgCI7s53Lx4tRbib2/X89dUMBEQTvzsWpmARBH7bU0l6rI+9Jc0J8t2WEnKKm7juvDj+83UWlfWd3L4whZe/7E+393JVS60m331gaCPBPq+KPiICXPnblWNQyI+8ON1YsZUV+aJR33i/NNQKNYtjLz6ZjzUkze16duXVEeHvSmTgyacG5jYV4KZ2xd/JOgW4prOWt7I+IsDZj8tjL8FRpaWqo4YndrwAiO0eH51wHwKCVcu/rIYDWAQLh1pLWVu2AYC/pFxLqGswrg79Due7azN5N+dTQBRf7k+/nSCXwc3nVv5WaNXGSeOgYE56MPOnRZz05/+jYY9eHBmLReDOVzbT0W3kn9ePI2gEvFnsiNjPRTtnCvZz0c6Zgv1cHBp7hsEpoKejhabvvwOzmexIDfmeOhb0ttUD2Ocayai2Iqt9mhzcGHfpecN6HD4ep78l20ghl8uYNymMkpp29uSLGQav3DmV5S9tGnR8SU07q7eVcv7EUL7eeIickmYE4MLJYVw8VVzoKGQy5hxHOYMdWy6YFMYFk8IAeOy6cXQbTDhpVNx0YSJvfCuWK/SJBQBGk1kqtdhX2EBJTTuTk/xwcFDw0QCxAOBQVRtltR2E+x+540aCVwxx3pHMDZpFrOfRMwpqmrr4euMhTCYLOnctB0qbuHJOzFHN1zq6jfzfl/soqxWdxF8eUH/d2il6ahzu93A04jwHbyfn5+TLQxP+arUtwNmPxyc9yFvZH7E45mIUctv3SvYW84tSvBNpN3SwvWa31LHg7jG3EOkeRl5ToSQWjPFJ4ZywWQQ6D971o6KuQxILksI98XTVsHFfFd9tKcHDRU1nj5H0OB98h+HaYzJbsFgEq84kdv5YFFW10tFtZProALtYYMeOHTt27Jzh2DMMhomOlgaq/to/sc+I9mETc3ig4BNp275L7mZC4Xo8Zs1CJgi0FR5CPXoMnqFBp+OQh43TodTpDWZKa9uJCHCV0qJ/2VVOW6eBGamB/LC1lBmpgTz87g7SY3XI5TJ2HOw39Xv25kl4uWlO6TH/WWnt0KNSytmaU8snv+RL219cPoWWdj2Pvb8TgMnJfpjMAtsP1DIrLYi4EA/++78sBECrVnD7JSl09LZ0HIpjPRcr6jt45J0dDHZhOmt0AFefE2ez/fNfC9i4r4oeg232UJDOGWetktyyFpQKOfcvSSUy4MwwJTJbzLye9T4HGkUhRqvUcl/6ch7b9ow05tWZz9js19fBY9O+KjbtF8sfFp4VKbU0vfXFDXTrrb+La8+NY0KCLz9uK6Wz28ScccH4HGNqc25pM5v2V7M1pwYHlZxrzo5jYpLfCX3m0409ejE4TW09uLuo+einPDZkVnHHwhRGRY1si9A/O/Zz0c6Zgv1ctHOmYD8Xh8ZuengSNHY3YbAYyazLYlrQJJxU1lG0g2++iGLHPqttGyOD2SKbgatHPrfs3MbmqEAWLn8EV6c/Xs3tmfrDs1gEbnx2PQqFDOOALhQzxgRy1dzY03hkf156DCZueWEjILaM9HbTUFJjfe5EBLjy4JVpyOUyuvUm7nh5EyZz/yUkLVbHrfOTB339o52LRpOZgopWvlxfZPO+A4kKcuPmi5LwcFFTWNnKkx/ttnr+nPEhnDchlP/7Yh9FVbYtKZ21Kjp7jAgC6Nw13LN49GnP/LEIFt7J/oTM+iwCnf0lD4RpgRNZHDtfGicIAu1dRlZlFLNuT39/9hAfZx5dOk56vL+okZe+sL7uAQR6O1HZ0Ck9fuf+GZIPhSAIdOlNOKqVog+BRSC3rJnqxi4rMamPB5aMISbY/eQ//CnmTL0mnk5+3lHG57/1m3j6ejryr+vHnTIfjD8r9nPRzpmC/Vy0c6ZgPxeHxl6ScIIc3g5RbzZwcVR/CcGWiu14DxALMoM9CWprRUicyiPjxvLY+wLPjHXFXxX+hxQLzmTkchkxwW5Sh4NpowKYlRZEoPeRDffsjBwaByUv3jaZ91bnUlTZKi3aPVzUNLeLJQvXnRsn+W9o1UoevmYs763OpbhaXJjvzqtn+Usb+cfV6fh6Dr0I33Gwlh6DmTExOrKLG3nz2wNWz/t6aHnixgnoDWaKKlvp0ptYu7uCwopWCita+WBNLi6OKjKyaqR9fNy13HFpimTa+OBVadz8/AYMvYLUlGR/WjsNZB1qlPapb+nh818LuX1hysl+fSeFXCYn0j2MzPosSSz416S/4akRSzCMJjNvfneAgyXNdOlNNvtfPtu6bCI5wpN5k8L4fkuJ1faBYgFARlZNbyaQjM1Z1Xz//+3dd5xU5b3H8c9s78sWFpYFFhbWR6oUEVBpEtFYEo0lthi9lqgvLNHYY0lyvZqYlzcvY4LBRO9NTDRGjT0GuQYJSpUizWfpLG0LW9he5/4xs8Muswvb57Dzff/DzJwzw2/gN+ec53ee8sUeEmIj+K9bprHg3U1s3lXk2zc1MYpJp/SnsrqeZRsP8vHKvd1aMGhsdPPh8t0MS0/o0VVOglWVN2+aT9i6dMMBVn+d3+L/GeC8KUNULBARETkJqIfBCbz29VsUL11CTYSL7UM9Xdibuu/WNtSy+sE76F/iuUj65ehvU18XD2G1XDVzHPOmDCEnt4Tn3/yKb509nHlT+uY4eSdX6nbsL+WFtzcSGRHK49+fQkyUamRO4Xa7yS+uoryqjqqaep5/ayPfmZnF+VOHtrp/QUkVP/vfNZRX1QGQGBvBM7dNbzFfQEpKHG8s+poDhyta3B0/1sTsVK48Z2SrY+7ziit52Lu0WJO46HAunZnF2ePSCQ87fiOnrr6BN5fsZNXWPK48ZyQfrdjD/oKWjeizx6UzdcwAzJB+vdposkXbeX79Qt/zpmNZQ2Mjt/xiCQAxkWG+gsGEkanccvHoE67YsTevjBCXiw07Cnnrs53ERYczbfQAFn+5r92xxUSG8et7Zvh6Izz60goKSqp4+LrJDBsYf9zVMtpr5ZY835waAJkD4nngmonkFVfyzKtrfYWfjNRYbrpoFMMGtpw3o7ishtioMPYXVvD2ZzvYefAI47JSuHzWCFKbDb1w8jGxK8qr6li0ei919Y0M7h/HmOHJ7DlUxt68MrbsLmb7/lIaGt1k9I+lf2I0aUnRLGo2j8/5U4dy2ogUNu8u5sLpmR2e60M6rq/mopx8lIviFMrFtmlIQie99MZTzFq0DYCKqBA+OjuBGy56kJSoJHYu+5iwV98B4A/jpzE882wuPms4q7fmcdGZw4Lm7ol+eNIdmk+EeDxut5vfvbeZVVvzOWNUGtfNM75JBw+V1vDIgs99+6anxHDwcKXv+dzJg7n23BMvX/rS+1tYvvkQ508dyrisFLLSE4iM6FzjpqCkigdfXN7qtsjwUO67agIjMxIpLa9h4ftbGJYez+WzRnRLA/lYje5Gfr3uJXJKdjAvcw7fHvFNauoa+OPHluWbPT0pFtw3i+raBr7YeJBvnD64Xf8nzdXVN1JT10BkeAgbth9m16Ej5OSWsGN/60M3nr3jTPYcKqN/v+gWq5Ss317YYpWNWy8ezbQxnZ/ToNHt5ok/rPLrAXEi35mZxYzx6ZSU1/rm2zhWUnwkT944hfgYTy+yk/mY2Oh2U1hS5VtWr7ishn5xkYSEuPjDh1ta9LhpzgXERof7CnrNXTozi4kjU8noH9sjeS1tO5lzUfoW5aI4hXKxbSoYdEJ+RSEbn3qYIflHL4BWjI2hKDGMkAFpTNpWR9rGPeyOT+H1ARey4L5ZQXnHRD886W1f7ynmF6+t8z0/ZXAi2UP6kRAfxWuLjq6y8MI9MwgLDeHTtfuZcVo6sVHh7fr8uvoGCkurfUMPuqqkvIbFa/YxIiOBd/+9i7355cfdf1xWCndeNs5XdHS73ew8cISaugZOzUwipFmjy+12syevjNTEaFZuyWNidirJCcef0LOqvorI0EiWrDvAG59u991Zv/58w+wJGV38tq0rLa9hT14ZozKTOFRURX1DI6mJUb5Gdms+XbuPVxd55jbISI3lZzdP9W3LzS+nX1wE0ZFhLYqzjY1uXnxvMwXFVXxz2lAiwkPZe6iMzzYcoLishjPHDmT0sCRKK2r5+9Kd1De4Pf9m8VEkxEWweedhkhOiWLElz/eZ/eIiKCmvbRFbVEQoV8/NZtGaXPYXVBAXHc6TN04hPCyEwvI6hqREn3RF49Vf57PgnU2tbnO5wO32FHlmnJbOP1YcXWLzjFFpXHPuKSTERFBZXcfWPcXk5pczIDmGYQPju+13JB2n87M4hXJRnEK52DYVDDph6R9/w8Clrd9RalIeHcJHZ3yPH39vTi9F5Tz64Ulvq29o5AfPLml1lQPwNGqGpyfwwytP69W4OqK+oZEPl+/h3WW7fK/NmZjBv9YdHUoxMTuVOy8bzz9X7eWv3gnj0pKiefrWaXy24QDbckvZX1DeogARGuLipgtH8dGKPdTWNXLFnJFMNv39/v7K6jrme5cjjQgP4YrZI5kzMcM3f4RT1Dc08uPfryS/uIrn5p9Fv7jIFsNGpo8ZyGWzskhOiGJ/QTm/fWdTi14lx3r61mm+uS/2FZQTHRHW6oopdm8xf/4kh8NHqlusBvHs7WdSUFKFGdrPM3Gj281Cb4+X5uZOGsy181rvzVJT18B/v7GBnFzP/CqPff/0Ey4b2uh2s2lnEbFRYWQNSuj2O/UHD1fw6Esr/V4PCw2hvuHopLE/vPI0xmWlUFjqGU50uLSGSaekqueAQ+n8LE6hXBSnUC62TQWDDqqtqmLrffOJrm3gjcyzKG9M4j9yP/Db74NRI7nk6rvIGnT8i72+TD88CYTq2nrcbs/4+beX7mTXwSPUN7iZMymDa79xCi4XJ0UjpqGxkR37j9DY6ObUzCRKymv48yc5fGkLAP9hFQDJCZEUHalp99/hcsHvfjTbd8e76Eg1j/5+JTXeJSJffuicbvo2PeO1xdv4ZE0u159nqKqpZ+22Ar8hDmeMSmP9tkJfb4kByTHkFXn+3UZmJDIsPZ5zTx/i62rfXgcPV/DMn9dSVlnH1XOzObeVuWjKq+q494XPWzSsAWaMT2f2xAwiwkN5fXEOsdHhzJqQwcoteSzdcMC3X/9+UTx1y7Tj9kj40yLrm5dj2pgB3Hzh6G4t7vzm7Y18mVNA1qAErp6bTXVdA0lxkQxMjqGgtIq/fLKN86cOZVRmUrf9ndLzdH4Wp1AuilMoF9umgkEHbXjuWaK3bGZPRiKzH3uOXQfLqHzmfmJqPMMTGlwulqaNJSd9Ks/cduYJJ0Lry/TDEydwu90UV9WTEBl60nUFb01rd3yfuW06DzWbD2HogDgumJZJaEiIrxfB9n2l/O69zYSGujhjVBoffLGnxWd895yRrMspIGdfKQCzJ2Zw/XnOXmZ0b14ZT75y/N5eTaaNHsBls0b4eg1U19YTFdH1yU5P9Dn7C8rZefAIkeGhDBucxEO/WXbcz4uPCee+707gnX/vYv32Qt/rD1w9kVMzkzhSUcuS9fuJiw7n3WW7KKv0nxvg6rnZDEmLIy46nGUbDzIyI5FJpj/b95VyoLCCcVkphIa6eOWjr6msruP6808lNTGKDTsK2bK7GBcwuH8c67cXYveWMCQtjsdvOP2kKLRJ++j8LE6hXBSnUC62TQWDDsq5+QYAvhqfweV3PQXAqwuf5YxVnhm2X8u8gNjsbL43zxx3abdgoB+eOEVfy8V9BeUsXrOP9dsLuWTGcGZ7705/uHwP+cWVzL9sHGOH+y8P6Ha7cQMhLheHiip5ZOEK/w8HFtw7q9OTOfamRrebu371b7/lHm+/ZCzvLduFywWHiirJHBDPw9dNDviwiv7947E7CvjL4m2szfH0FElLiiY6Ioz0lBiSE6KYPXEQqYnR5OaX88TLq9r1uU/dMpWaugZeX7zNV/DpLtGRodx+ydhW80lOXn3tmCgnL+WiOIVysW0qGHTQpttuJKLezZEnfsTpQ8YCkHMgjxc/eofRRTF8/95rOjx7eF+lH544hXKxdVU19ew+eITy6nrfpHYTRqZy1+XjAxxZ++WXVLF1dxFTRw/gSGUdBwoqmJCd6tteUV1HTGSYI+6ON8/D2roGcnJLyBqUQEwbk25+unYfDY1uNu8q4qsdh1tsmzt5MHvzyrhunmFIWpzv9fXbC3lryQ6SEiLJL6qipKIGl8vlG2YSGxVGRbWnwJKaGMXA5Bg27SoCji5vuHjNPmKjw0hLimHG+PTjTkApJycdE8UplIviFMrFtqlg0EF/em4+pYkpzL/pJ4EOxfH0wxOnUC6e2OHSavbmlWGG9muzAStd09k8rK1rYNHqXIYPSmBtTgFZ6QmcNS693e+vqqknv7iKzIHxftuazvVOKKhI79ExUZxCuShOoVxsW1sFg64P7Oyjxlz+Q9LiEgMdhohIt0pJjGp1VQAJvIjwUC46cxgAY4Yld/j90ZFhrRYLQIUCERER6RwVDNowaeiIQIcgIiIiIiIiEjAn/1TiIiIiIiIiItLtVDAQERERERERET8qGIiIiIiIiIiIHxUMRERERERERMSPCgYiIiIiIiIi4kcFAxERERERERHxo4KBiIiIiIiIiPhRwUBERERERERE/KhgICIiIiIiIiJ+VDAQERERERERET8qGIiIiIiIiIiIHxUMRERERERERMSPCgYiIiIiIiIi4kcFAxERERERERHxo4KBiIiIiIiIiPhxud3uQMcgIiIiIiIiIg6jHgYiIiIiIiIi4kcFAxERERERERHxo4KBiIiIiIiIiPhRwUBERERERERE/KhgICIiIiIiIiJ+VDAQSWyVNgAACLJJREFUERERERERET8qGIiIiIiIiIiIHxUMpN2MMa5AxyAi4hTGGJ1DxRGUi+IUulYUJ1Aedq+wQAcgzmaMSQTGAqustXWBjkeClzcXs4H11tr6QMcjwckYEwXcBiyw1tYYY1zWWneg45LgY4xJAe4FfmKtrQ10PBK8vOfn6cC/rLU1gY5HgpPaLD1HFWlpkzHmUmAZcCfwvDHm1ACHJEHKGHMLsAJ4AngxwOFIcJsLPADc5H2uuxjS64wxo4G3gWjAbYwJDXBIEqSMMdcBnwHXAz83xkQHOCQJQmqz9CwVDOR4MoDHrLVXAYeA64wxp4C6+kjvMcZkAcOAc6y1FwOnGWNmercpD6VXNGuQ5QLvA5cbY7KttY3GGPXWk942FNhhrb3XWltnrW0IdEASfIwx4cAY4GZr7TXADKDpOlFtDOlNg1Gbpcfoxyw+xphhxpjZzV6aAoR7H78J1AGXAqgLrvSk5rlord0JvGmtPejd/CbwHe825aH0mGPysKlBNgv4HPgbcI93m4bISI9q5fycBWw0xgw0xiw0xtxnjBkRoPAkiBxzXKzDc614ljFmLLASGODd1hiwIKXPMx73NzvujQcivY/VZulmKhhIcy8CNxtjJniffwz8GMBauxlYD6QZY8YFKD4JHk25OB7AWruu2bYIPBclIj2tKQ/HNnttN9BorV2A55rlbWPMRQGJToJJUy6e5n2+D7gVTzfwpUCyd/vkAMUnwePYa8Un8DTO/glkAvcbY+7UtaL0BGOMyxhzN7AQT+H0UWPMBcBfUZulx6hgEOSauul4L0L6AVuAc70Teb0GVBhjHvTuvglP5Vjdb6XbtZGL85ptj/A+7Idn3G6L94l0hzby8PxmuwwHqr3jdicDE/BcKCsXpVu1dUz0np/fA4qBadbaV/E04qrwDCUU6VZtXSsCWGs/B/4ILAGeBO4CUvEcG0W6jTEmxNtboBS4w1p7O/AJMMtauxgoVZulZ6hgEKSMMd8wxiwEHvI2xDYDtwNfACkcbajdA8w3xgyx1u7AM8FXaiBilr7pBLmYaoyZC9BsFvB+wCfGmNONMU8D6oYrXdaOPDzXu2sB8AbwA+ACPJNx3gDq9ijdowPn57vxzOkSa63NxdPLILK1zxTpjBPlYtP5GU/uTbLWrrbWbsWTh5qlXrpFszx8wBgTh6c3wRbvZjcQ7318H2qz9AgVDIKQMSYdTxX4HTyT0zyG50C/Dk9FLheYboxJttauwlM5fsQY8yGQAOQEJHDpc9qZizOMMQne/WOBgcArwC+Apdba7QEIXfqQdubhWd78WwfMttbOsNYuB17g6IWLSJd08Py8GvgL8FNjzJve/b8OSODS53Tg/Jxkrd0L5BhjXjXG/AkYB9jARC59yTF5aID7gTHNCvTb8RT1I621K1GbpUe43G7dEAkG3hm+78ZzNywJmGit/U9jzDA8k4IkAz+31pYbY6YCFwObrLWve9+bAMy01r4bmG8gfUUnc3GDtfZv3gbbHuBxa+1vA/MNpC/oRB5+C/jSWvt288/Q7PTSVV08P4cBcXjOz+8F5htIX9HFXIwCzgKGWmtfCcw3kL6gg3n4IyDNWvtAs/cnAnOste/0fvR9k3oYBAFjzHQ8Y3ymAN8E5gPfNcbEW2t3A2uAWOBC71vW47lLcaYxJsta22CtLVaxQLqqC7k4wxhjrLUVwBAVC6QrOpmHW4HZxrPMJ9Bi5QSRTumG83O9tbZExQLpqi7m4ghrbbW19v9ULJCu6EAeXux9Sw3wgTHmcmPMl8aYadbaUhULupcKBsHBDbxsrb0aeAvPkmAleLrSAmwAdgKDvF16avB0u10LlAUgXum7upKLRQDW2qpej1r6Gh0TxSmUi+IUXcnFIwGIV/qm9uZhuvf5xXjmNPg2cKu1dkXvhhscNHNkcPgS+Mr7uBzIBq4CVhtjplhrVxtjDgCXeE8ATUuSbA5ItNKXKRfFCZSH4hTKRXEK5aI4QXvz8FJjTDieZbZfbD5cULqf5jAIQsaYfwCXATfjqcg9DMzGs/zIg9ba+sBFJ8FEuShOoDwUp1AuilMoF8UJjpOHA6219wYwtKCiHgZBxhiTjWdykErgeWNMLZ4fYBZwt04A0luUi+IEykNxCuWiOIVyUZzgRHkY0OCCjAoGwScM+LsxZhTwDPCFtfbRAMckwUm5KE6gPBSnUC6KUygXxQmUhw6hgkHwmQj8FE93nv+x1r4a2HAkiCkXxQmUh+IUykVxCuWiOIHy0CFUMAg+ZcAjwHPW2tpAByNBTbkoTqA8FKdQLopTKBfFCZSHDqFJD4OMMcZlrdV/ugScclGcQHkoTqFcFKdQLooTKA+dQwUDEREREREREfETEugARERERERERMR5VDAQERERERERET8qGIiIiIiIiIiIHxUMRERERERERMSPCgYiIiICgDHmCu+fk40xDwQ6HhEREQksrZIgIiIiGGPCgB3W2sxAxyIiIiLOEBboAERERMQRfgkMNcYsA14GbrXWTjPG3ACcC8QAWcD7eHooTgeSgSustTnGmATgRWCQ9/Nutdbm9PJ3EBERkW6kIQkiIiKCtfYe759nAzuP2TwbuBY4G7gXyLfWzgHeA+7w7vM48JG1djbwK+C5no9aREREepJ6GIiIiMiJrLHWVgIYY/KBT72v7wDO8z4+B5hpjLkZCAfqez1KERER6VYqGIiIiMiJVB3zvLbZY1ezP++y1q7onZBERESkp2lIgoiIiDSpNMbEd/K9S4Armp4YY/p3S0QiIiISMCoYiIiISJPfAsuA7E6890lgkDFmuTFmDXBjdwYmIiIivU/LKoqIiIiIiIiIH/UwEBERERERERE/KhiIiIiIiIiIiB8VDERERERERETEjwoGIiIiIiIiIuJHBQMRERERERER8aOCgYiIiIiIiIj4UcFARERERERERPyoYCAiIiIiIiIiflQwEBERERERERE//w9GChdjLqfQVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = VieAlgorithmNav(hold_df)\n",
    "\n",
    "plt.rcParams['font.family']='serif'\n",
    "\n",
    "df.plot(figsize=(18,8),\n",
    "                   title='优选基金超配组合的绝对收益情况')\n",
    "\n",
    "# 此时的基准为HS300\n",
    "print_table(strategy_performance(df[['基金重仓股等权组合','HS300']],'HS300'))\n",
    "\n",
    "print_table(get_return_year(df,'HS300'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def View_Industry(hold_fund:pd.DataFrame):\n",
    "    \n",
    "    industry = QueryIndustry(hold_df)\n",
    "    group_industry = industry.groupby('industry')['RET'].mean()\n",
    "    \n",
    "    plt.rcParams['font.family']='serif'\n",
    "    \n",
    "    \n",
    "    fig = plt.figure(figsize=(12,10))\n",
    "    fig.add_subplot(1,2,1)\n",
    "    group_industry.sort_index().plot.barh(color='#DC143C',title='优选基金超配组合的行业配置情况')\n",
    "    plt.legend(['历史平均收益'])\n",
    "    plt.axvline(0,color='black')\n",
    "    \n",
    "    fig.add_subplot(1,2,2)\n",
    "    industry['industry'].value_counts(normalize=True).sort_index().plot.barh(title='行业配置占比',color='#6495ED')\n",
    "    plt.legend(['历史行业配置'])\n",
    "    \n",
    "\n",
    "def QueryIndustry(hold_df:pd.DataFrame)->pd.DataFrame:\n",
    "    \n",
    "    hold_fund = hold_df.copy()\n",
    "    hold_fund['symbol'] = hold_fund['symbol'].replace(r'\\D','',regex=True)\n",
    "    df_list = []\n",
    "\n",
    "    for rptdate, df in hold_df.groupby('period_end'):\n",
    "\n",
    "        stocks = GetOverWeightStock(df)\n",
    "\n",
    "        # 格式化股票代码\n",
    "        # if len(code) >= 6 过滤港股 前十大中有港股数据\n",
    "        stocks = [normalize_code(code) for code in stocks if len(code) >= 6]\n",
    "        \n",
    "        ## 获取行业\n",
    "        ind = get_industry(security=stocks, date=rptdate)\n",
    "\n",
    "        ind = {\n",
    "            x: v.get('sw_l1').get('industry_name', np.nan)\n",
    "            for x in ind.keys() for v in ind.values() if 'sw_l1' in v.keys()\n",
    "        }\n",
    "    \n",
    "        start_date = OffsetDate(rptdate.strftime('%Y-%m-%d'), 15)\n",
    "        \n",
    "        end_date = OffsetDate(rptdate.strftime('%Y-%m-%d'), 75)\n",
    "        \n",
    "        # 模拟季度调仓 日度持仓\n",
    "        close = get_price(stocks, start_date, end_date,\n",
    "                          fields='close', panel=False)\n",
    "        \n",
    "        ret_df = pd.pivot_table(close, index='time',\n",
    "                                columns='code', values='close').pct_change()\n",
    "        \n",
    "        ret_df.columns = list(map(lambda x: ind.get(x, '其他'), ret_df.columns))\n",
    "        \n",
    "        ret_ser = ret_df.stack()\n",
    "        \n",
    "\n",
    "        df_list.append(ret_ser)\n",
    "\n",
    "    ret_ser = pd.concat(df_list)\n",
    "    ret_ser.index.names = ['date','industry']\n",
    "    ret_ser.name = 'RET'\n",
    "    return ret_ser.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "View_Industry(hold_df)"
   ]
  }
 ],
 "metadata": {
  "hide_input": true,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "MarkDown菜单",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
